THE OPTIMAL DEDUCTIBLE AND COVERAGE IN INSURANCE CONTRACTS AND EQUILIBRIUM RISK SHARING POLICIES

In this paper, we consider the optimal risk sharing problem between two parties in the insurance business: the insurer and the insured. The risk is allocated between the insurer and the insured by setting a deductible and coverage in the insurance contract. We obtain the optimal deductible and coverage by considering the expected product of the two parties' utilities of terminal wealth according to stochastic optimal control theory. An equilibrium policy is also derived for when there are both a deductible and coverage;this is done by modelling the problem as a stochastic game in a continuous-time framework. A numerical example is provided to illustrate the results of the paper.

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.

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.

Emotion Deduction from Social Media Text Data Using Machine Learning Algorithm

Emotion represents the feeling of an individual in a given situation. There are various ways to express the emotions of an individual. It can be categorized into verbal expressions, written expressions, facial expressions and gestures. Among these various ways of expressing the emotion, the written method is a challenging task to extract the emotions, as the data is in the form of textual dat. Finding the different kinds of emotions is also a tedious task as it requires a lot of pre preparations of the textual data taken for the research. This research work is carried out to analyse and extract the emotions hidden in text data. The text data taken for the analysis is from the social media dataset. Using the raw text data directly from the social media will not serve the purpose. Therefore, the text data has to be pre-processed and then utilised for further processing. Pre-processing makes the text data more efficient and would infer valuable insights of the emotions hidden in it. The preprocessing steps also help to manage the text data for identifying the emotions conveyed in the text. This work proposes to deduct the emotions taken from the social media text data by applying the machine learning algorithm. Finally, the usefulness of the emotions is suggested for various stake holders, to find the attitude of individuals at that moment, the data is produced. .

A Comparison between Deductive and Inductive Approaches for Teaching EFL Grammar to High School Students

This study had the purpose of testingtwo methods for teaching grammar in Englishasa Foreign Language(EFL)class:the deductive and inductive approaches in terms of effectiveness and rapport.This research was conducted in a public high school in Ecuador.Seventy students enrolled in the second year of senior high school participated.One in-service teacher taught the EFL classes during the process of intervention(10 weeks),and two EFL teachers observed all of these classes and recorded the information by filling in observation sheets.The students were administered grammar pretests and post-tests in order to assess their grammar knowledge.The results of the tests showed a significant difference in the scores in favor of the inductive approach.After the statistical analysis of the data obtained from the tests and observation sheets,we concluded that the inductive approach is more effective for teaching grammar in the EFL classroom in terms of instruction and rapport.

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.

助力还是阻力:税收优惠政策与企业数字化转型

税收优惠政策在推进企业数字化转型中发挥了助力还是阻力,这是亟须研究的重要课题。基于2008—2021年中国上市公司数据,考察税收优惠政策对企业数字化转型的影响及作用机制。研究发现:税收优惠能有效促进企业数字化转型,且可以通过促进创新产出、缓解债务风险及优化融资环境的路径提高企业数字化转型水平。进一步研究发现,将税收优惠区分研发费用加计扣除和固定资产加速折旧这两种不同优惠方式,加计扣除与加速折旧对企业数字化转型的影响效应具有显著差异,加计扣除与企业数字化转型呈倒“U”型关系,而加速折旧显著促进了企业数字化转型,且两者之间没有形成“1+1>2”的协同效应。异质性检验发现,税收优惠促进企业数字化转型的作用在非国有企业、内部控制质量较高和成熟期企业较为明显。研究结论有利于厘清税收优惠政策助推企业数字化转型的作用机制,促进企业加快数字化转型。

未来5~10年中国医疗保障待遇设计的改革思路

医疗保障待遇包括起付线、报销比例、封顶线等内容。典型国家社会医疗保险报销起付线多设置在人均年收入的3%以内,对门诊、住院和药剂服务设立了不同的支付政策,医疗费用个人自付占可支配收入的比例在0.25%~2%。借鉴国际经验,展望中国社会医疗保险制度未来的发展,建议提高门诊保障待遇,以可支配收入为基数设置报销起付线,不同层级医疗机构实行相同的报销比例,设置个人自付封顶线,取消门诊报销封顶线,门诊和住院共用个人最高自付限额。

基于GIS与大数据分析的矿压监测预警平台

为响应国家关于加快煤矿智能化发展的号召,针对煤矿GIS“一张图”中巷道路径不能实现精确计算,矿压数据不能实现高效甄别、有效提取的问题,以及矿压预警需要实现在线推演、可视化展示的需求,提出建立基于GIS与大数据分析的矿压监测预警平台。采用GIS“一张图”、巷道结点-路径拓扑算法、多源异构监测数据处理技术、矿压大数据分析技术,搭建成能够实现矿井下复杂情况切实反映、矿压监测数据有效提取、采掘风险在线预测、可视化程度高的矿压监测预警平台。通过在李雅庄矿的实际应用,表明该系统运行稳定可靠,可有效提升煤矿的矿压风险监测预警。

基于Bookmark标准设定方法的数学演绎推理能力表现研究

为了深入分析八年级学生的数学演绎推理能力表现情况,了解不同性别、不同学校地域学生的表现差异.通过Bookmark方法开展标准设定,基于IRT技术获得学生数学演绎推理能力表现的水平比例.结果显示,有30%的学生达到最高水平,有超过17%的学生处在低水平,尚未达到课程标准要求.总体表现中女生成绩略好于男生,但在水平分布上呈现明显差异性,女生较男生更稳定.城市和县镇学生的能力表现明显好于农村地区学生,3类地域的学生水平分布也呈现明显不同,农村地区高水平人数比例最低,且不合格水平人数比例高于高水平学生比例.结果表明需要关注数学演绎推理能力在性别、地域上的表现差异,教育测量评价研究中开展标准设定工作对测评结果分析有重要作用.

基于知识图谱的城市洪涝灾害链推演及时空特性解析

城市洪涝灾害具有链式传播特性,解析城市洪涝灾害链的传递规律和时空演变特征对于阻断灾害链具有重要意义。本研究以郑州“7·20”特大暴雨洪涝灾害中地铁5号线被淹和京广快速路隧道被淹2个事件为案例,构建知识图谱揭示灾害链传递规律。通过复盘灾害链的演变过程,从中识别灾害链中的诱发点、引爆点、扩散点以及放大点,并着重分析致灾机理以及灾害链的时空特性,以灾害曲线形式直观展示不同灾害链的影响程度。郑州“7·20”洪涝灾害典型案例解析表明,两事件存在多个灾害链阻断时机,在救灾过程中应综合考虑灾前、灾中因素的影响,果断采取断链措施,并且要充分发挥应急措施的时效性,及时阻断引爆点和扩散点,降低洪涝灾害损失。该方法能够应用于链式灾害的复盘,指导分析具体事件的致灾机理和救灾切入点。

基于情景推演的铁路降雨灾害预测性防控方法

铁路降雨灾害类型众多且十分容易引发铁路事故,现有的降雨量等单一指标监测、阈值式报警方式,导致铁路降雨灾害误报率、虚报率居高不下。为了有效应对铁路降雨灾害监测预警难、风险预测不准确以及防控措施选择难等关键问题,提出一种创新的基于情景推演的预测性防控方法。首先,依据铁路事故报告和降雨灾害的成因机制,将12种铁路降雨灾害分为五大类情景;接着,基于扎根理论为每种情景类型识别并分类相关的情景要素,运用情景削减方法为每种情景类型构建最优情景集和防控策略集,从而形成一个包含5类情景的情景网络。其次,为了满足铁路防灾决策需求,建立了灾害计划推演预测和实时推演预测2种模型,并提出一种将情景网络转换为灾害贝叶斯网络的方法,通过将实际情景与情景网络进行精确匹配进行推演预测,并根据“情景-应对”方法选择合适的防控策略。最后,以石庙沟车站的滑坡灾害为例进行了实例分析和验证,结果显示,通过本方法进行的推演预测能够准确反映灾害的发展过程,推演得到的情景与实际灾害发展情景高度一致,情景相似度达到93%,灾害发生的概率为88%。采取相应防控措施后,滑坡灾害的发生概率显著降低至12%,列车脱轨的可能性也从70%降至1%,证实了本方法的准确性和实用性。该研究可为铁路降雨灾害的预测与主动防控提供重要的理论与技术支撑。

研发费用加计扣除政策对制造企业创新投入效果评估--基于双重差分模型的检验

为鼓励企业创新发展,政府加大了研发费用加计扣除力度以及扣除范围。要想准确地评估政策实施效果,既要考虑政策对创新投入的影响,还要关注政策实施的异质性。本文以2012-2022年制造业上市公司为研究对象,采用DID方法评估了研发费用加计扣除政策实施前后对制造业企业创新投入的影响。从实施效果来看:研发费用加计扣除政策通过缓解融资约束进而促进了制造业企业的创新投入。进一步研究发现:实施效果存在明显的异质性,对东部地区、国有制造业企业、非高技术及低发展能力制造业企业的创新投入实施效果最为显著,但对西部地区、非国有制造业、高技术和高发展能力制造业企业创新投入效果欠佳。此研究针对切实帮助制造业企业转型升级,更好地实现政策助企纾困的目标提出对策建议。

面向数字孪生变压器的计算机辅助运维(CAM+)构架与关键技术

发展数字孪生变压器对支撑电力装备智能运维具有重要价值。目前变压器已具有计算机辅助设计、工程仿真的一般功能,但面向运行维护的场景、性能数字孪生还处于初级阶段。为此,该文提出了一种将计算机辅助设计、工程仿真、制造进一步拓展为电力装备的计算机辅助运维(computer aided maintenance+,CAM+)新模式,包含了场景更新、虚实融合、故障定位、智能运维等功能。该文首先介绍了CAM+的内涵,结合电力装备的数字孪生架构提出了面向数字孪生变压器的CAM+模式的技术架构。然后,综述了3维场景可视化建模、高精度传感、多物理场耦合模型与快速计算、虚实融合的动态推演、多源数据融合、状态智能评估、知识图谱等CAM+模式所涉及的关键技术。最后,分析了CAM+在变压器场景更新、参量孪生和智能运维的应用,并总结了未来CAM+所面临的挑战和发展方向。

带多级免赔额的风险保费及其贝叶斯估计

在汽车保险中,保单设计常常会提供多个级别的免赔额以供被保险人选择.因此,在汽车保险的聚合风险模型中,将索赔额按大小分为若干级别.进而,建立了在均值-方差保费原理下风险保费的贝叶斯模型,并获得了风险保费和贝叶斯估计的显式解.研究结论显示:在一定条件下,贝叶斯估计能表达为样本估计和聚合估计的加权平均.同时,研究了风险保费的线性贝叶斯估计,获得了在任意分布下风险保费的最优信度估计,证明了贝叶斯估计和信度估计的强相合性和渐近正态性.最后,利用数值模拟方法验证了估计的大样本性质.

风险约束与最优保险:一种更符合投保人期望的保险契约

在保险实践中,投保人往往期望在发生保险事故之后能够获得保险公司的充分赔偿,从而确保他们的实际经济损失被严格控制在预先设定的、可接受的阈值之内。为了满足这类保险需求,本文考虑当投保人损失不大于某个非负特定值时,在Arrow模型的基础上引入投保人的净损失约束以研究投保人的最优保险问题。具体的,本文在投保人的低损失区间而非全损失、高损失区间引入净损失约束,既考虑了投保人效用的帕累托改进,同时又使最优保单含有对投保人合理避险的激励。研究表明:投保人效用最优时,如果低损失区间上的净损失约束不起作用,则Arrow模型解就是本模型的解;反之如果起到作用,则给出了本模型的一个特殊解,并且指出该特殊解存在两个免赔额。此外,投保人的期望效用会随着净损失上限的增大和小损分界点的减小而有所提高,但当净损失上限增大或小损分界点减小到一定程度使得投保人低损失区间上的净损失约束不起作用时,投保人效用达到最大而不再进一步提高。

应用伦理学中的原则伦理

在应用伦理学中原则主义展现了如何将伦理应用到具体实践的一条进路。不同于诉诸一种基于某一最高原理并拥有普遍适用性的道德系统的演绎论,也不同于依赖以感知判断为基础的类比性评价的决疑论,原则伦理者寻求处于抽象的道德理论与具体的道德实践之间的所谓中位原则,为人们应对伦理冲突提供价值基准与论证依据的规范性导向框架。如果说不伤害、有利、公正、自主性属于比较经典的伦理原则的话,那么人权、尊严、隐私、责任则属于新近出现的并得到广泛认可的伦理原则。应用伦理学中的中位原则具有不证自明的品格,其实质根基便是人类的道德直觉。这些原则之间处于协调的融贯关系,其内容在实践中需得到具体化的解释和依情境的灵活权重。

智能时代二次创作的著作权保护与限制研究

智能时代,二次创作产业勃兴发展,原始作品激励与二次创作自由急需一个平衡经济效益和法律正义的协调机制。本文建议:首先,应加快著作权集体管理组织和相关制度的完善,有效对接互联网环境下的著作权集体管理,强化网络服务经营者的监管义务,优化授权许可模式;其次,将“三步检验法+转换性使用”的判断标准纳入合理使用判断体系之中,更加科学合理地判断互联网环境下的二次创作;再次,对于未经许可的二次创作作品,应对其进行著作权法消极保护,赋予其一定的禁止性权利,限制其积极性权利的行使,同时通过法律设计引导市场对未经许可的二次创作进行调节,减少司法资源浪费。