Optimizing Social Impact in Organizations:Concepts,Levers and Barriers

With the socio-economic change that has taken place over the last years,in addition to an increase in sustainability regulation,stakeholders have gained importance and organizations are more active in relation to generating social impact,but society demands more and better social impact from organizations.The objectives of this paper are to clarify the concepts of impact and social impact optimization,and to detect levers and barriers to help organizations optimize the social impact that they generate.A qualitative approach based on interviews with social impact leaders from organizations with different forms(big companies,small and medium-sized enterprises,corporate foundations,b-corps,community foundations,public and private foundations,associations and investing firms)is applied,together with focus groups with stakeholders from those organizations that are best practices.

Near resonance vibration isolation on a levered-dual response(LEDAR)Coulomb-damped system by differential preloads/offsets in linear springs

The levered-dual response(LEDAR)Coulomb-damped system attains near resonant vibration isolation by differential preloads/offsets in linear springs.It takes the advantages of both the preloads/offsets in linear springs and the guiderail friction for realizing different levels of vibration isolation.The isolation capacities are investigated on the strategies with both the horizontal and vertical guiderails,with the horizontal rail only,and without guiderails.The compressive preloads generally result in the consumption of most of the initial excitation energy so as to overcome the potential threshold.The isolation onsets at the frequency ratio of 1∓0.095 on the left-hand side(LHS)and the right-hand side(RHS)of the lever are relative to the load plate connector.The observed near resonant isolation thus makes the LEDAR system a candidate for the isolation of the mechanical systems about resonance while opening a path for simultaneous harvesterisolation functions and passive functions at extreme frequencies.

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.

Design a Special Fixture for Tractor Lever

Aiming at the problems of long time and poor machining precision in processing the tractor lever workpiece,a special fixture suitable for the tractor lever workpiece is designed by analyzing the processing technology of the lever workpiece,and the workpiece is accurately positioned and tightened under the action of the special fixture.Efficient machining of the workpiece can be achieved by using a drill template.The fixture not only has the advantages of high production efficiency,low cost and high life,but also effectively improves the processing technology of the workpiece,and has certain reference value for the subsequent fixture improvement design.

Morphological differences of hindlimb levers between wild and farmed American mink(Neovison vison)and implications for reintroduction of mustelids

Reintroduction is an important strategy to restore or re-establish wild populations of endangered species.Pre-release training is a necessary step to ensure postreintroduction survival.However,studies reported contradicting outcomes after pre-release training of juveniles and adults.This study used farmed and feral American mink(Neovison vison)to analyze the influence of captive breeding on the morphology,structure and efficiency of the two major hindlimb levers,the femur and tibia pivoted by hip and knee joints that are essential for locomotion.Results showed that captive breeding did not alter the sexual dimorphism of the two levers that are related to survival in the wild.Captive-bred mink showed slightly altered morphology of the femur and fundamental structure of the hindlimb levers that improved efficiency,but this resulted in reduction of performance related to foraging in both terrestrial and aquatic environments,especially for females.These findings suggest that reintroduction of mustelid as exampled by the mink here should focus on juveniles because the skeletal alterations associated with captive rearing were recorded only among adults and are irreversible in adulthood.In contrast,captive-reared juveniles showed no skeletal alterations and would be expected to recovery from any atrophy of the muscular system caused by captive rearing for shorter durations.Our results support the application of pre-release training of juveniles in enriched environments as a method for alleviating structural alteration of appendages and enhancing locomotion to increase survival probability in complex habitats.

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.

魅摄人物 Martin Levers

商业保险经纪人,工作之余还需兼顾家庭,但Martin仍挤出时间创作出大量优美的风光摄影作品。

China Engages Monetary Levers

Driven by a steady increase in both food and non-food prices, the expected growth of consumer price index （CPI） for September was 2.5 percent year on year. This is a gain on the August level of 2.3 percent year on year. Upward pressures on price leading up to the Chinese National Day holiday in early October and rising crude oil prices globally also contributed to an upward adjustment in prices.

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.

平行级联光杠杆测量金属丝杨氏模量

光杠杆能放大微小变化量,在原子力显微镜,精密天平和杨氏模量测量等领域都有重要的应用。级联光杠杆能够在有限空间内提高传统光杠杆的放大倍数而受到广泛关注,但仍存在不容易确定反射级次,入射光与光杠杆倾斜平面必须有一定初始夹角等问题。平行级联光杠杆通过在垂直于光杠杆倾斜面的平面放置多个光杠杆,形成多次反射提高放大倍数,每个镜面进行一次反射,易确定反射级次,并且入射光与光杠杆倾斜平面可无初始夹角。研究分析了平行级联光杠杆的光路及测量原理,确定了计算公式,并通过测量金属丝杨氏模量进行了实验验证。利用望远镜观测的方法,通过三级平行级联光杠杆,放大倍数可达103.7倍,与单个光杠杆相比,提高了一个数量级。

“一带一路”共建国家银行业系统性风险的测度研究

有效识别“一带一路”共建国家的系统性风险及其关键驱动因素对平稳实现各国经济金融的深度融合具有重大的现实意义。基于150多万条银行微观股票数据和财务数据,通过构建压力时期回归模型的客观方法获得系统性风险三因素权重,在同一框架下利用规模、杠杆、关联性3个因子测算了银行、国家、“一带一路”共建国家系统3个层次的银行业系统性风险指数。研究结果表明,从系统性风险表现来看,“一带一路”共建国家整体的银行业系统性风险受全球宏观金融环境波动影响显著。此外,“一带一路”共建国家也会因为自身问题而出现银行业系统性风险上升的情形。印度、越南、印度尼西亚等国家是系统重要性国家,其银行业系统性风险水平较高;匈牙利、克罗地亚、罗马尼亚等国家的银行业系统性风险水平较低。银行业系统性风险较高的国家主要在于其有较高系统性风险贡献水平的银行,且各行业经济发展状况以及经济增长对国家层面系统性风险有显著的影响。从风险驱动因素看,关联性因子是驱动银行业系统性风险水平变化的主要因素,其次为规模因子和杠杆因子。

位移放大型黏滞阻尼装置的减震效果研究

为解决普通黏滞阻尼器性能在层间位移较小的结构或位置难以得到充分发挥的问题,提出一种基于杠杆原理将位移和速度放大的黏滞阻尼装置,该装置通过放大普通黏滞阻尼器两端的相对位移,进而放大相对速度,使阻尼器性能得以充分发挥,更好地达到降低结构地震响应的效果。以某实际工程结构为背景,通过SAP2000有限元软件建立有限元模型,分析原结构、附加普通黏滞阻尼器结构和附加放大型黏滞阻尼器结构在3条地震波作用下的地震响应。结果表明,在3条地震波作用下,相较于附加普通筒式黏滞阻尼器结构,附加放大型黏滞阻尼器结构的层间位移角和基底剪力降低幅度均较大,说明放大型黏滞阻尼器相较于普通黏滞阻尼器有更好的减震效果。

利用光杠杆测透镜的曲率半径

基于光杠杆测量物体微小形变的思想,结合光杠杆与迈克耳孙干涉仪移动平台,搭建了测量透镜曲率半径的装置.利用光杠杆足尖位置和平凸透镜所在球面的几何关系,推导出曲率半径的测量公式,并测量了不同规格透镜的曲率半径.实验结果表明:使用该装置测量的曲率半径与利用牛顿环装置的测量值相近.

基于两个点集间参数估计的DVL标定方法

[目的]为了提高无人艇(USV)捷联惯性导航(SINS)与多普勒计程仪(DVL)组合导航的精度,需要对DVL误差进行精确标定。[方法]首先,对DVL的安装误差角、杆臂、比例因子进行误差建模,着重分析舰船中杆臂误差项对组合导航定位产生的影响;然后,提出一种基于两个点集间参数估计的DVL误差标定方法,即将SINS/GNSS导航参数与DVL输出参数视为两个点集,将标定问题转换为两个点集间差值的估计问题,再利用卡尔曼滤波器对两个点集间的误差参数进行估计;最后,利用基于奇异值分解(SVD)的可观测性分析,对不同运动条件下滤波器的可观测性定量分析,给出标定过程中载体的运动策略。[结果]数学仿真和海试试验结果表明,标定后的SINS/DVL算法全局相对精度可达到0.122%航程;在角机动时,杆臂会导致SINS/DVL定位突变;补偿杆臂后的定位误差更加平滑,且整体上具有更小的定位误差。[结论]所提方法可为包括杆臂在内的各项DVL误差标定提供可行的途径。

基于放大机构的压电叠堆泵设计分析及试验

为了克服压电叠堆输出位移较小、对泵振子泵送能力弱的缺点,综合杠杆铰链与三角形铰链结构性能优点,提出基于放大机构的压电叠堆泵,分别开展理论放大倍数分析、ANSYS仿真分析、流固耦合分析及泵送性能试验测定研究.结果表明,压电叠堆泵仿真放大倍数约为20.3时,与理论计算值相对误差为11.7%;ANSYS仿真分析表明,泵振子的二阶频率对应的弯振为最佳振型,该振型对应的工作频率为1356.30 Hz,在该工作频率下对压电叠堆泵进行流固耦合分析,得出泵送流量为41.78 mL/min.在上述研究分析的基础上试制压电叠堆泵样机并进行泵送性能试验,发现在驱动电压50 V下,测得泵送流量为32.50 mL/min,仿真流量与试验流量相对误差为28.5%,进而分析了误差产生的原因,最大泵送压差值为13.7 mm.试验结果具有良好的稳定性,验证了仿真数据结果的可靠性,可为压电泵新型放大机构和压电叠堆驱动方式的进一步研究提供可靠参考.

某埋头弹机枪拨弹杠杆动力学分析及优化

某埋头弹机枪使用了与传统枪弹结构完全不同的埋头弹,为了适应这种弹药,该埋头弹机枪与传统机枪在结构上也有很大的差异。其中,相较于传统机枪,该埋头弹机枪的输弹行程更为靠后,为了减小全枪长度,不得不缩短拨弹杠杆中曲线槽部分的长度。这使得枪机框上的导柱进入拨弹杠杆曲线槽时,对拨弹杠杆的冲击力很大,从而对整个输弹机构的可靠工作造成极为不利的影响,同时也会严重降低枪机框的动能,使得输弹动作难以完成。现以导柱与拨弹杠杆之间的最大冲击力为目标函数,选用5次函数曲线作为拨弹杠杆曲线槽理论轮廓线的形状,运用遗传算法对该理论轮廓线进行优化,使得导柱与拨弹杠杆之间的最大冲击力降为优化前的1/3。试验验证表明,枪机框在进入拨弹杠杆曲线槽的过程中,速度损失由原来的0.94 m/s降低到0.28 m/s,达到了优化目的,该研究结果为该枪的改进提供了可靠的理论依据。

矿用FBG倾角传感器结构设计及传感性能分析

基于杠杆—摆锤结构与力学分析方法,利用差动补偿法消除温度对光纤布拉格光栅(FBG)波长漂移量的影响,建立波长与倾角传感的数学模型,分析矿用FBG倾角传感器传感特性。通过矿用FBG倾角传感器平台标定静态实验分析总结,铜基体表贴FGB传感器精度为1.526%、灵敏度为952.95 pm/(°)、重复性为1.2687%、迟滞性为0.2998%,结果表明:FBG倾角传感性能与实际倾角测量结果具有较好的一致性,为矿用FBG倾角传感器的应用提供理论技术支撑。

Multi-level access control model for tree-like hierarchical organizations

An access control model is proposed based on the famous Bell-LaPadula （BLP） model.In the proposed model,hierarchical relationships among departments are built,a new concept named post is proposed,and assigning security tags to subjects and objects is greatly simplified.The interoperation among different departments is implemented through assigning multiple security tags to one post, and the more departments are closed on the organization tree,the more secret objects can be exchanged by the staff of the departments.The access control matrices of the department,post and staff are defined.By using the three access control matrices,a multi granularity and flexible discretionary access control policy is implemented.The outstanding merit of the BLP model is inherited,and the new model can guarantee that all the information flow is under control.Finally,our study shows that compared to the BLP model,the proposed model is more flexible.