Pool bio-oxidation and fitting analysis of low-grade arsenic-containing refractory gold ore

To overcome the limitations of geography,climate,and ore characteristics on the ore beneficiation process,biooxidation studies on low-grade arsenic-bearing refractory gold ore by pool leaching were carried out,as well as process fitting analysis.The gold particles are encapsulated by pyrite and arsenopyrite.After 60 days of biooxidation,the oxidation rates of arsenic,sulfur,and gold were 39%~69%,24%~41%,and 49%~83%,respectively.The inoculated Acidithiobacillus ferrooxidans,Ferroplasma acidiphilum,and Leptospirillum ferrodiazotrophum could all mediate the initial pyrite/arsenopyrite oxidation and the Fe^(2+)oxidation reaction,but only the former could mediate the subsequent sulfur compound oxidation.When compared to daily bacterial circulation and bacterial replacement every ten days,aeration improved the gold leaching rate by 14%~22%.The Boltzmann model could fit both the arsenic and sulfur bio-oxidation,with model fit variances greater than 0.98.Based on the experimental and fitting results,the bio-oxidation cycle was determined to be 60 days,and the bio-oxidation mechanisms are summarized.This study has significant practical implications for the rational utilization of gold resources and provides theoretical and practical guidance for similar gold ores.

A Pooling Method Developed for Use in Convolutional Neural Networks

In convolutional neural networks,pooling methods are used to reduce both the size of the data and the number of parameters after the convolution of the models.These methods reduce the computational amount of convolutional neural networks,making the neural network more efficient.Maximum pooling,average pooling,and minimum pooling methods are generally used in convolutional neural networks.However,these pooling methods are not suitable for all datasets used in neural network applications.In this study,a new pooling approach to the literature is proposed to increase the efficiency and success rates of convolutional neural networks.This method,which we call MAM(Maximum Average Minimum)pooling,is more interactive than other traditional maximum pooling,average pooling,and minimum pooling methods and reduces data loss by calculating the more appropriate pixel value.The proposed MAM pooling method increases the performance of the neural network by calculating the optimal value during the training of convolutional neural networks.To determine the success accuracy of the proposed MAM pooling method and compare it with other traditional pooling methods,training was carried out on the LeNet-5 model using CIFAR-10,CIFAR-100,and MNIST datasets.According to the results obtained,the proposed MAM pooling method performed better than the maximum pooling,average pooling,and minimum pooling methods in all pool sizes on three different datasets.

Effects of pooling,specialization,and discretionary task completion on queueing performance

Pooling,unpooling/specialization,and discretionary task completion are typical operational strategies in queueing systems that arise in healthcare,call centers,and online sales.These strategies may have advantages and disadvantages in different operational environments.This paper uses the M/M/1 and M/M/2 queues to study the impact of pooling,specialization,and discretionary task completion on the average queue length.Closed-form solutions for the average M/M/2 queue length are derived.Computational examples illustrate how the average queue length changes with the strength of pooling,specialization,and discretionary task completion.Finally,several conjectures are made in the paper.

Bayesian partial pooling to reduce uncertainty in overcoring rock stress estimation

The state of in situ stress is a crucial parameter in subsurface engineering,especially for critical projects like nuclear waste repository.As one of the two ISRM suggested methods,the overcoring(OC)method is widely used to estimate the full stress tensors in rocks by independent regression analysis of the data from each OC test.However,such customary independent analysis of individual OC tests,known as no pooling,is liable to yield unreliable test-specific stress estimates due to various uncertainty sources involved in the OC method.To address this problem,a practical and no-cost solution is considered by incorporating into OC data analysis additional information implied within adjacent OC tests,which are usually available in OC measurement campaigns.Hence,this paper presents a Bayesian partial pooling(hierarchical)model for combined analysis of adjacent OC tests.We performed five case studies using OC test data made at a nuclear waste repository research site of Sweden.The results demonstrate that partial pooling of adjacent OC tests indeed allows borrowing of information across adjacent tests,and yields improved stress tensor estimates with reduced uncertainties simultaneously for all individual tests than they are independently analysed as no pooling,particularly for those unreliable no pooling stress estimates.A further model comparison shows that the partial pooling model also gives better predictive performance,and thus confirms that the information borrowed across adjacent OC tests is relevant and effective.

U-Net Based Dual-Pooling Segmentation of Bone Metastases in Thoracic SPECT Bone Scintigrams

In order to enhance the performance of the CNN-based segmentation models for bone metastases, this study proposes a segmentation method that integrates dual-pooling, DAC, and RMP modules. The network consists of distinct feature encoding and decoding stages, with dual-pooling modules employed in encoding stages to maintain the background information needed for bone scintigrams diagnosis. Both the DAC and RMP modules are utilized in the bottleneck layer to address the multi-scale problem of metastatic lesions. Experimental evaluations on 306 clinical SPECT data have demonstrated that the proposed method showcases a substantial improvement in both DSC and Recall scores by 3.28% and 6.55% compared the baseline. Exhaustive case studies illustrate the superiority of the methodology.

Expanding the liver donor pool worldwide with hepatitis C infected livers, is it the time?

Liver transplantation(LT)provides a life-saving option for cirrhotic patients with complications and hepatocellular carcinoma.Despite the increasing number of liver transplants performed each year,the number of LT candidates on the waitlist remains unchanged due to an imbalance between donor organ supply and the demand which increases the waitlist time and mortality.Living donor liver transplant had a great role in increasing the donor pool and shortened waitlist time for LT candidates.Nevertheless,further strategies can be implemented to increase the pool of potential donors in deceased donor LT,such as reducing the rate of organ discards.Utilizing hepatitis C virus(HCV)seropositive liver grafts is one of the expanded donor organ criteria.A yearly increase of hundreds of transplants is anticipated as a result of maximizing the utilization of HCV-positive organs for HCV-negative recipients.Direct-acting antiviral therapy's efficacy has revolutionized the treatment of HCV infection and the use of HCV-seropositive donors in transplantation.The American Society of Transplantation advises against performing transplants from HCV-infected liver donors(D+)into HCV-negative recipient(R-)unless under Institutional Review Board-approved study rules and with full informed consent of the knowledge gaps associated with such transplants.Proper selection of patients to be transplanted with HCV-infected grafts and confirming their access to direct-acting antivirals if needed is im-portant.National and international consensuses are needed to regulate this process to ensure the maximum benefit and the least adverse events.

Simulation of single bubble dynamic process in pool boiling process under microgravity based on phase field method

We use the phase field method to track the gas-liquid interface based on the gas-liquid two-phase flow in the pool boiling process,and study the bubble nucleation,growth,deformation,departure and other dynamic behaviors on the heating surface under microgravity.By simulating the correlation between liquid undercooling and bubble dynamics,we find that the bubble growth time increases with the increase of liquid undercooling,but the effect of liquid undercooling on bubble height is not significant.Meanwhile,the gas-liquid-solid three-phase contact angle and the gravity level will also have an effect on the bubble growth time and bubble height.With the increase of the contact angle,the bubble growth time and bubble height when the bubble departs also increase.While the effect of gravity level is on the contrary,the smaller the gravity level is,the larger the bubble height and bubble growth time when the bubble separates.

Genotyping ex situ trees of Abies nebrodensis translocated from the original Sicilian population to enrich the gene pool

As a dynamic ex situ conservation strategy,a clonal seed orchard was started in a nursery in Pomaio(POM)in central Italy in 1993 for an assisted migration experiment of Abies nebrodensis(Lojac.)Mattei.Two artifi cial ex situ populations were planted with this gene pool:a seedling arboretum in Pieve Santo Stefano(PSS)and a small dendrological collection in Papiano(PAP),both originating from the Sicilian relict population.Here,using AFLP markers,we estimated the relatedness among the relocated genotypes of the three collections to check whether the three collections had suffi cient genetic variability to be considered as additional sources of variability to the original gene pool for the assisted migration strategy.High individual genetic variability was found in the collections;each plant had a diff erent genotype and was confi rmed to belong to its population of origin.PAP and PSS trees were shown to be only from the original population of A.nebrodensis species and were derived from a limited set of maternal fertile genotypes.Based on the Sicilian fi r population inventory,nursery production in Sicily,and structure clustering analysis,close genetic relationships among POM,PAP and several PSS trees(1–35)were evident.Similarly,the PSS group(36–78)was genetically close to tree 1 of POM and in a lesser proportion to plants 7 and 9 of POM.The sampling of seedlings used to form batches in the nursery might have infl uenced the structure of the resultant plantations.All genotypes will be useful for enriching the original gene pool.

Comparisons of Structured Surface Floors for Pool Boiling Enhancement at Low Heat Fluxes: Hands-On Learning Setup for Heat Transfer Classroom

Various enhanced surfaces have been proposed over the years to improve boiling heat transfer. This paper introduces an experimental setup designed for boiling demonstration in the graduate-level Heat Transfer course. The pool boiling performance of water under atmospheric pressure of 1.025 bar is investigated by using several structured surfaces at heat fluxes of 28 and 35 kW/m2. Surfaces with holes, rectangular grooves, and mushroom fins are manufactured by an NC-controlled vertical milling machine. The heat flux versus excess temperature graph is plotted by using thermocouple measurements of water and base temperatures of the boiling vessel. The separation, rise, and growth of individual vapor bubbles from the surface during boiling were recorded with a digital camera. The results for the plain surface are compared to the Rohsenow correlation. The enhancement of heat transfer coefficient (h) ranged between 15% - 44.5% for all structured surfaces. The highest heat transfer coefficient enhancement is observed between 41% - 56.5% for holed surface-3 (405 holes) compared to the plain surface. The excess temperature dropped around 29% - 34% for holed surface-3 (405 holes) compared to the plain surface. The heat transfer coefficient increases as the spacing between channels or holes decreases. While the bubbles on holed and mushroomed surfaces were spherical, the bubbles on the flat and grooved surfaces were observed as formless. The suggested economical test design could be appropriate to keep students focused and participating in the classroom.

Pooled Sputum for Xpert MTB/RIF Testing: A Cost-Saving Strategy in Mwanza, Tanzania

Background: Tuberculosis (TB) diagnostics, such as Xpert MTB/RIF, is still limited by cost. Testing of pooled samples from presumptive TB patients has been thought as a cost-saving strategy to diagnose TB. We assessed the utility and cost-saving of pooled Xpert MTB/RIF testing strategy for the diagnosis of TB in Mwanza, Tanzania. Methods: Sputum samples from Presumptive TB patients were submitted to TB laboratory for routine diagnosis of TB using Xpert MTB/RIF. The TB results from the individual sputum samples were used as the reference standard and were concealed to the investigating laboratory technicians. The remainder of samples were collected serially and were pooled (5 samples per pool) for testing. The agreement of the results between individual sample testing against pooled sample testing and cost-savings was assessed. Results: A total of 250 sputum samples from presumptive TB patients were analyzed and 50 pools were made with each pool containing 5 samples. Of the 50 sputum pools made, Mycobacterium tuberculosis (MTB) was detected in 17 (34.0%) pools. Results from the individual sputum samples MTB/RIF testing were retrieved for all 250 samples and there were 28 (11.2%) samples in which MTB was detected whereas 222 (88.8%) samples had no MTB detected. Following re-analysis of positive pools, all 28 (100%) individual positive MTB samples were detected within the 17 positive pools, with 1 to 3 individual MTB positive samples per pool. The individual sputum samples were correctly identified by pooled sputum on Xpert MTB/RIF testing, with the sensitivity of 100% and specificity of 100%. Using pooling of sputum samples strategy, we saved 46.0% (115/250) of the cartridges. Conclusion: The pooled sputum testing strategy reduced cartridge costs by 46.0% and has the potential to increase the affordability of Xpert MTB/RIF testing in countries with limited resources, such as Tanzania.

基于改进Faster R-CNN的热轧带钢表面缺陷检测

热轧带钢是钢铁行业的重要产品,其表面缺陷是影响产品质量的重要因素。针对传统缺陷检测算法存在的过程繁琐、精度不足和效率低下等问题,提出一种基于改进更快速区域卷积神经网络(faster region-based convolutional neural network,Faster R-CNN)的检测算法,实现对热轧带钢表面缺陷的高效、高精度检测。首先,采用特征相加的方法对底层细节特征和高层语义特征进行融合;然后,采用精准的感兴趣区域池化(precise region of interest pooling,Precise ROI Pooling)获取固定大小的特征向量,避免特征出现位置偏差;最后,利用均值偏移聚类算法对带钢数据集进行聚类,获得适用于热轧带钢表面缺陷检测的先验框尺寸。实验结果表明,所提算法在热轧带钢表面缺陷检测数据集上的平均精度均值达到了85.34%,检测速度为23.5帧/s,且鲁棒性良好,满足实际的工业检测需求。

表面张力对激光焊接熔池及匙孔的影响

基于FLUENT19.0软件,建立了激光焊接热-流耦合模型,对比分析了不同表面张力温度系数(为负值)对熔池流场的影响.结果表明,随着表面张力温度系数的减小,熔池后方顺时针漩涡的流动趋势逐渐减弱,甚至消失,而且焊接飞溅的数量增多.纵截面熔池长度逐渐增加,纵截面熔池流体最大流动速度逐渐增大,熔池横截面的面积逐渐减小.当表面张力温度系数为-2.5×10^(-4)N/(m·K)时,熔池长度平均值为3.28 mm、熔池流体最大流动速度的平均值为2.89 m/s、熔池横截面面积的平均值为4.52 mm^(2);当表面张力温度系数为-3.5×10^(-4)N/(m·K)时,熔池长度平均值为3.73 mm、熔池流体最大流动速度的平均值为3.53 m/s、熔池横截面面积的平均值为4.03 mm^(2);当表面张力温度系数为-4.9×10^(-4)N/(m·K)时,熔池长度平均值为4.14 mm、熔池流体最大流动速度的平均值为4.09 m/s、熔池横截面面积的平均值为3.28 mm^(2).

基于自适应融合的实时车辆检测

针对传统的车辆检测技术检测速度慢和精度低的问题,提出了一种融合注意力的自适应金字塔网络的交通目标检测算法(fusion attentiont adaptive pyramid network,FAAP-Net),可以显著降低交通事故的发生率。为了降低计算复杂度,设计了一种轻量级的互补池化结构(CPS),该结构在宽度和高度上采用了两组不同的池化组合,在保持高精度的同时,显著降低了网络的浮点运算数(GFLOPs)和参数量。为了解决智能交通系统特征图生成过程中的信息损失问题,通过将自适应注意力模块(AAM)和特征增强模块(FEM)引入自适应融合特征金字塔网络(AF-FPN),以融入车辆检测的形状特征。针对车辆细节特征表征弱的问题,引入了一种按通道维度分组的注意力(SA)机制,以增强主干网络对不同车辆检测细节特征的关注,有效提取车辆细节的显著特征。在BDD100K数据集上的实验结果表明,FAAP-Net算法相比于传统算法,平均精度从30.3%提升到43.7%。

基于改进YOLOv8的煤矿输送带异物检测

现有基于深度学习的输送带异物检测模型较大,难以在边缘设备部署,且对不同尺寸异物和小目标异物存在错检、漏检情况。针对上述问题,提出一种基于改进YOLOv8的煤矿输送带异物检测方法。采用深度可分离卷积、压缩和激励(SE)网络将YOLOv8主干网络中C2f模块的Bottleneck重新构建为DSBlock,在保持模型轻量化的同时提升检测性能;为增强对不同尺寸目标物体信息的获取能力,引入高效通道注意力(ECA)机制,并对ECA的输入层进行自适应平均池化和自适应最大池化操作,得到跨通道交互MECA模块,以增强模块的全局视觉信息,进一步提升异物识别精度;将YOLOv8的3个检测头修改为4个轻量化小目标检测头,以增强对小目标的敏感性,有效降低小目标异物的漏检率和错检率。实验结果表明:改进YOLOv8的精确度达91.69%,mAP@50达92.27%,较YOLOv8分别提升了3.09%和4.07%;改进YOLOv8的检测速度达73.92帧/s,可充分满足煤矿输送带异物实时检测的需求;改进YOLOv8的精确度、mAP@50、参数量、权重大小和每秒浮点运算数均优于SSD,Faster-RCNN,YOLOv5,YOLOv7-tiny等主流目标检测算法。

基于条带池化与血管增强的眼底图像动静脉分类方法

视网膜血管动静脉管径比是定量分析糖尿病、高血压等慢性疾病的先决条件,是许多心血管疾病的重要风险指标。随着深度学习技术的发展,许多基于卷积神经网络的方法凭借其捕获高级语义的能力,在眼底图像动静脉分类方面取得了较大的进展。然而,这些方法大多是采用叠加局部卷积和池化操作方式,难以很好地应用于条带形状的眼底视网膜血管。在本研究中,为了更有效地提取条带形状的视网膜血管特征,引入条带池化来捕获空间像素远距离依赖关系,同时考虑到动静脉交错复杂的特性,结合空间金字塔池化并提出了一种全新的混合池化技术以扩大神经网络的感受野和学习上下文信息。另一方面,考虑到眼底图像中血管与非血管分布的比例极不平衡,引入了血管增强模块,利用血管分布信息和高斯核函数约束的血管边缘的信息作为权重校正动静脉特征抑制背景特征,进而解决血管与背景分布比例不平衡问题。在分别包含40、22、45张彩色眼底图像的3种国际公开数据集DRIVE、LES和HRF上的实验表明,所提算法的平衡精度(BACC)分别为0.955、0.946、0.967,表明本研究结合条带池化与血管增强的方法能够较好解决眼底图像中动静脉交错复杂和类别不平衡问题,实现对眼底视网膜动静脉的精确分类,具有较高的应用价值。

基于摆动激光扫描的GMAW焊缝成形调控

将摆动激光与GMAW工艺复合,利用“8”字形摆动激光扫描液态熔池,对其温度场和流场进行调控,以改变熔池温度梯度,主动调控熔池流动以改善焊缝成形.在6061铝合金板材表面进行堆焊试验,重点探究了光丝间距、摆动幅度、激光功率对于焊缝成形的影响,采用高速摄像机拍摄激光扫描熔池过程.结果表明,摆动激光扫描可以有效抑制成形缺陷的发生,当摆动激光扫描熔池中部时可以获得最佳的焊缝成形效果.摆动激光扫描降低了熔池温度梯度分布,同时激光扫描产生的激光蒸发反力可以驱动熔池流动,促进了液态熔池的流动铺展,从而有效抑制了不规则焊缝成形缺陷,减少焊缝内部气孔和裂纹缺陷.调节摆动激光摆幅可以在一定范围内对焊缝宽度进行调控.

养老保险省级统筹对养老保险基金平衡的影响研究

提高基本养老保险统筹层次是增强养老金支付能力的重要改革举措,也是实现养老金制度安全规范发展的关键。文章基于养老保险省级统筹改革的准自然实验,利用2008-2019年中国A股上市公司数据,构建双重差分模型实证评估了养老保险统筹改革对企业实际缴费率及基本养老保险基金平衡的影响。研究发现:省级统筹显著降低了企业养老保险实际缴费率,该结果在更改变量度量方式、排除竞争性解释、考虑异质性处理效应等一系列稳健性检验中仍然成立。养老保险省级统筹在不同征管模式、企业类型及地区层面对企业养老保险实际缴费率存在异质性影响,对于社保征收和税务代征模式、劳动密集度高的企业、所在地区老龄化程度低和财政压力小的企业影响更明显。机制分析发现,市县政府由于“相对剥夺效应”和“逐底竞争效应”所产生的征管强度不足是造成省级统筹改革后企业缴费率下降的重要因素。进一步分析表明,省级统筹改革降低了基金收支平衡,并对中西部省份影响更明显。研究对于加强地方政府征管治理、促进养老保险基金平衡发展具有重要启示,为征管主体间利益关系的平衡及养老保险全国统筹的顺利推进提供了经验证据。

华北局地大暴雨过程中多个β中尺度对流系统发生发展对比分析

利用常规地面高空观测、多普勒雷达、风廓线、VDRAS(Variational Doppler Radar Analysis System)和NCEP再分析资料,对2018年8月5—6日副热带高压(以下简称副高)控制下华北一次局地大暴雨过程中多个β中尺度对流系统触发和发展机制进行了分析。结果表明:这次大暴雨发生在副高控制下,处于高温、高湿气团中,大气层结极不稳定。暴雨由多个相继发展的中尺度对流系统造成,分别是太行山迎风坡上西南—东北向、华北平原地区保定一带南北向、保定至霸州附近西南—东北向和以雄安新区为中心东西向原地生消的准静止MCS-Ⅰ、MCS-Ⅱ、MCS-Ⅲ和MCS-Ⅳ,均属于β中尺度。在相似的环境中,不同中尺度对流系统触发机制有较大差异,太行山迎风坡上的MCS-Ⅰ是由近地层偏东暖湿气流在迎风坡与山风形成的辐合抬升触发;由辐射差异和前期强降水形成的局地冷池受MCS-Ⅰ影响再次加强后,其出流与环境风形成的两条地面辐合线分别触发了MCS-Ⅱ和MCS-Ⅲ,并组织对流沿辐合线呈带状发展;而超低空偏东风增强叠加冷池出流在地形抬升作用下促使沿山暖湿气团进一步抬升,使得原本消亡的MCS-Ⅰ再次重建。MCS-Ⅳ发展最旺盛、持续时间最长,是大暴雨中心的直接制造者,一方面MCS-Ⅱ与MCS-Ⅲ、MCS-Ⅰ与MCS-Ⅳ的两次合并过程,是MCS-Ⅳ增强、持久的重要原因;另一方面边界层偏东风急流为MCS-Ⅳ的发展提供了水汽和不稳定能量等有利条件,同时推动其左前方中尺度涡旋的发展,导致MCS-Ⅳ所在地的气旋性涡度大大增加,加强了以急流轴为中心的垂直次级环流发展,造成MCS-Ⅳ的发展维持,形成华北平原地区以雄安新区为中心的东西向大暴雨带。

不同秸秆还田模式对冬小麦土壤活性有机碳库的影响

为探寻最适宜小麦种植的秸秆还田模式,优化土壤结构、提升土壤质量,采用根箱培养,以不施肥(CK)为对照,设置不同秸秆还田方式配施化肥共6个处理,即秸秆还田(SF)、秸秆生物炭还田(BF)、秸秆配施腐熟剂还田(SDF)、秸秆配施纳米载体腐熟剂还田(SNDF)以及单施化肥(F).结果表明:所有处理在不同时期的土壤活性有机碳库组分质量分数基本表现为随土层的加深而降低,SNDF处理在小麦成熟期的表层土壤总有机碳(SOC)、土壤可溶性有机碳(DOC)、土壤微生物量碳(MBC)质量分数分别为36.40 g/kg、491.56 mg/kg、273.10 mg/kg,土壤碳库管理指数为225.7%,SDF在小麦成熟期的土壤活性有机碳(LOC)质量分数为7.79 g/kg,SOC、土壤DOC、土壤MBC、土壤LOC和碳库管理指数之间均存在极显著的正相关性(P<0.01).研究发现SNDF处理对冬小麦土壤有机碳组分及土壤碳库管理指数的改善效果最好,该结果为秸秆还田与化肥配施在农业生产上的应用提供了理论依据和研究基础.

“抱团取暖”可以应对“寒冬”吗? 贸易政策不确定性与企业协同创新 ——来自中美贸易摩擦的经验证据

基于2018年美国政府发起的对华贸易战,运用广义双重差分模型考察贸易政策不确定性对企业协同创新的影响及其作用渠道与经济后果。研究发现:贸易政策不确定性通过加剧融资约束、降低风险承担能力的作用渠道驱动中国企业选择“抱团取暖”,即以“协同创新”的方式谋求技术突破和研发攻关以抵御外部风险。调节效应检验结果发现:当上市公司属于高新技术企业时,贸易政策不确定性对协同创新的驱动效应更为显著;数字化转型为企业间创新要素的聚拢和流动提供了便利,较高的知识产权保护力度降低了企业协同创新中知识被模仿和窃取的风险,因此两者均能够强化贸易政策不确定性对企业协同创新的驱动作用。进一步区分协同创新的主体后发现:当企业面临贸易政策不确定性时,更倾向于选择与供应链上下游企业、高校及科研机构进行协同创新。并且,贸易政策不确定性在促使企业选择协同创新后可显著提升企业未来一期、二期、三期的全要素生产率,从而证实“抱团取暖”的协同创新方式确实能够帮助企业安稳度过“寒冬”。本文丰富了协同创新的驱动机制研究,也为企业在急剧变化的外部贸易环境中以“抱团”方式寻求理性出路提供了参考与借鉴。