术前IDEAS模式访视在肺癌胸腔镜辅助小切口手术患者中的应用效果

目的 探讨术前IDEAS模式访视在肺癌胸腔镜辅助小切口手术(VAMT)患者中的应用效果。方法根据干预方式的不同将80例肺癌VAMT患者分为对照组(n=34)和IDEAS组(n=46),对照组患者给予常规术前访视,IDEAS组患者给予术前IDEAS模式访视。比较两组患者的生理指标[脉率(PR)、舒张压(DBP)、收缩压(SBP)]、疼痛程度[视觉模拟评分法(VAS)]、心理状态[汉密尔顿焦虑量表(HAMA)、汉密尔顿抑郁量表(HAMD)]、希望水平[Herth希望量表(HHI)]、满意度、住院时间及并发症发生情况。结果 干预后,两组患者PR、DBP、SBP均高于本组干预前,IDEAS组患者PR、DBP、SBP均低于对照组,差异均有统计学意义(P﹤0.05)。干预后,两组患者VAS、HAMD、HAMA评分均低于本组干预前,HHI量表各维度评分均高于本组干预前,IDEAS组患者VAS、HAMD、HAMA评分均低于对照组,HHI量表各维度评分均高于对照组,差异均有统计学意义(P﹤0.05)。IDEAS组患者的总满意率为100%,明显高于对照组患者的82.35%,住院时间明显短于对照组,差异均有统计学意义(P﹤0.01)。IDEAS组患者的并发症总发生率为4.35%,与对照组患者的11.76%比较,差异无统计学意义(P﹥0.05)。结论 术前IDEAS模式访视能有效改善肺癌VAMT患者的生理指标、心理状态和疼痛程度,提高希望水平,患者的满意度高。

内镜息肉冷圈套切除术联合IDEAS护理模式访视用于结直肠息肉切除患者的效果观察

目的探讨内镜息肉冷圈套切除术(CSP)结合识别问题-确定选项-评估并选择最佳选项-行动-检验有效性(IDEAS)护理模式访视应用于结直肠息肉患者的临床疗效,并分析对患者疼痛因子的影响,为提高该疾病的临床治疗效果提供依据。方法选取2021年1月至2023年1月南方医科大学南方医院增城院区收治的112例结直肠息肉患者进行前瞻性研究,根据不同的手术方式分为A组[56例,IDEAS护理模式访视+内镜息肉热圈套切除术(HSP)]和B组(56例,IDEAS护理模式访视+CSP),术后随访6个月。比较两组患者息肉切除情况,术前和术后3d的疼痛因子指标及血清血栓素B2(TXB2)、血管内皮生长因子(VEGF)、碱性成纤维细胞生长因子(bFGF)水平,以及息肉完整切除率和术后6个月并发症发生情况。结果B组患者手术时间短于A组;与术前比,术后3 d两组患者血清前列腺素E2(PGE2)、神经生长因子(NGF)、TXB2、VEGF、bFGF水平均升高,但B组均低于A组;血清P物质(SP)水平均降低,且B组低于A组(均P<0.05)。两组患者内镜操作时间、息肉切除时间、息肉直径、息肉切除数量、息肉完整切除率及术后6个月并发症总发生率比较,差异均无统计学意义(均P>0.05)。结论CSP结合IDEAS护理模式访视应用于结直肠息肉患者的治疗可显著缩短手术时间,患者术后疼痛较轻,有利于减轻机体炎症反应,促进疾病转归。

基于IDEAS的多元化护理对溃疡性结肠炎患者生活质量及满意度的影响

目的探究基于IDEAS的多元化护理模式对溃疡性结肠炎患者生活质量及满意度的影响。方法方便选取2018年9月—2023年9月盐城市第三人民医院消化内科收治的84例溃疡性结肠炎患者作为研究对象,以随机数表法分为两组,对照组(42例)实行常规护理,观察组(42例)实行基于IDEAS的多元化护理模式。比较两组生活质量评分及护理满意度。结果观察组生活质量评分高于对照组,差异有统计学意义(P<0.05)。观察组护理总满意率远高于对照组,差异有统计学意义(P<0.05)。结论基于IDEAS的多元化护理模式对溃疡性结肠炎护理效果具有积极影响,利于提高患者生存质量与护理满意度。

Reliability evaluation of IGBT power module on electric vehicle using big data

There are challenges to the reliability evaluation for insulated gate bipolar transistors(IGBT)on electric vehicles,such as junction temperature measurement,computational and storage resources.In this paper,a junction temperature estimation approach based on neural network without additional cost is proposed and the lifetime calculation for IGBT using electric vehicle big data is performed.The direct current(DC)voltage,operation current,switching frequency,negative thermal coefficient thermistor(NTC)temperature and IGBT lifetime are inputs.And the junction temperature(T_(j))is output.With the rain flow counting method,the classified irregular temperatures are brought into the life model for the failure cycles.The fatigue accumulation method is then used to calculate the IGBT lifetime.To solve the limited computational and storage resources of electric vehicle controllers,the operation of IGBT lifetime calculation is running on a big data platform.The lifetime is then transmitted wirelessly to electric vehicles as input for neural network.Thus the junction temperature of IGBT under long-term operating conditions can be accurately estimated.A test platform of the motor controller combined with the vehicle big data server is built for the IGBT accelerated aging test.Subsequently,the IGBT lifetime predictions are derived from the junction temperature estimation by the neural network method and the thermal network method.The experiment shows that the lifetime prediction based on a neural network with big data demonstrates a higher accuracy than that of the thermal network,which improves the reliability evaluation of system.

Hadoop-based secure storage solution for big data in cloud computing environment

In order to address the problems of the single encryption algorithm,such as low encryption efficiency and unreliable metadata for static data storage of big data platforms in the cloud computing environment,we propose a Hadoop based big data secure storage scheme.Firstly,in order to disperse the NameNode service from a single server to multiple servers,we combine HDFS federation and HDFS high-availability mechanisms,and use the Zookeeper distributed coordination mechanism to coordinate each node to achieve dual-channel storage.Then,we improve the ECC encryption algorithm for the encryption of ordinary data,and adopt a homomorphic encryption algorithm to encrypt data that needs to be calculated.To accelerate the encryption,we adopt the dualthread encryption mode.Finally,the HDFS control module is designed to combine the encryption algorithm with the storage model.Experimental results show that the proposed solution solves the problem of a single point of failure of metadata,performs well in terms of metadata reliability,and can realize the fault tolerance of the server.The improved encryption algorithm integrates the dual-channel storage mode,and the encryption storage efficiency improves by 27.6% on average.

基于IDEAS访视模式对腹腔镜下全子宫切除术患者生理、心理应激反应及应对方式的影响

目的:探讨基于IDEAS访视模式对腹腔镜下全子宫切除术患者生理、心理应激反应及应对方式的影响。方法:选取2021年1月—2022年12月于肇庆市第一人民医院拟行腹腔镜下全子宫切除术的患者60例作为研究对象,以随机数字表法分为对照组(实施常规术前访视)和观察组(实施基于IDEAS的访视模式),各30例。比较两组患者生理应激指标、心理应激水平、应对方式及护理满意度。结果:访视后,两组脉率、舒张压、收缩压水平高于访视前,观察组脉率、舒张压、收缩压水平低于对照组,差异有统计学意义(P<0.05);访视后,两组面对评分高于访视前,焦虑自评量表、抑郁自评量表评分及回避、屈服评分低于访视前,观察组优于对照组,差异有统计学意义(P<0.05);观察组护理满意度高于对照组,差异有统计学意义(P=0.0022)。结论:应用基于IDEAS访视模式于腹腔镜下全子宫切除术患者中的临床效果显著,有利于缓解患者的生理、心理应激反应,改善患者的应对方式,提高护理满意度。

Study of primordial deuterium abundance in Big Bang nucleosynthesis

Big Bang nucleosynthesis(BBN)theory predicts the primordial abundances of the light elements^(2) H(referred to as deuterium,or D for short),^(3)He,^(4)He,and^(7) Li produced in the early universe.Among these,deuterium,the first nuclide produced by BBN,is a key primordial material for subsequent reactions.To date,the uncertainty in predicted deuterium abundance(D/H)remains larger than the observational precision.In this study,the Monte Carlo simulation code PRIMAT was used to investigate the sensitivity of 11 important BBN reactions to deuterium abundance.We found that the reaction rate uncertainties of the four reactions d(d,n)^(3)He,d(d,p)t,d(p,γ)^(3)He,and p(n,γ)d had the largest influence on the calculated D/H uncertainty.Currently,the calculated D/H uncertainty cannot reach observational precision even with the recent LUNA precise d(p,γ)^(3) He rate.From the nuclear physics aspect,there is still room to largely reduce the reaction-rate uncertainties;hence,further measurements of the important reactions involved in BBN are still necessary.A photodisintegration experiment will be conducted at the Shanghai Laser Electron Gamma Source Facility to precisely study the deuterium production reaction of p(n,γ)d.

Does QM Embedded in 5th Dimensional Embedding Allow for Classical Black Hole Ideas Only in Early Universe, Whereas Corda Special Relativity Plus QM May Eliminate Event Horizons for Black Holes after Big Bang?

We first look at the possibility that the ideas of event horizons for black holes may have their application only in early universe conditions whereas Corda’s ground breaking work rejecting event horizons may be due to the formation of quantum mechanics free of an embedding in 5 dimensions allowing for a simpler more direct approach, which rejects the idea of a firewall. First, we present the idea of classical black hole physics applied only once as for the early universe, whereas in such a setting, there may be a way to present NLED and structure formation due to an initial entropy approach as outlined. Then the ideas of Corda’s breakthrough are presented for the reasons he illuminated in his recent work, due to QM being fully formed separate from higher dimensional embedding after the initial evolution of the universe.

BIG评分对接受去骨瓣减压术的中重度创伤性脑损伤儿童早期脑功能的预测价值

目的:探讨BIG评分(由格拉斯哥评分、国际标准化比值、碱剩余组成)对接受去骨瓣减压术(DC)的中重度创伤性脑损伤(TBI)患儿脑功能早期预后的预测价值。方法:回顾性分析2014年3月至2023年7月于我院接受DC治疗的所有中重度TBI患儿,以出院时儿童脑功能分类(PCPC)为结局,分为预后良好组(PCPC 1~2)和预后不良组(PCPC 3~6)。通过病历资料回顾,提取患儿的临床信息,并使用Logistic回归分析评估BIG评分的预测价值。结果:共纳入55例接受DC治疗的中重度TBI患儿,其中25例出院时脑功能良好,30例预后不良(包括9例死亡)。患儿入院时的高BIG评分(p < 0.001)、瞳孔对光反射差(p = 0.027),存在失血性休克(p = 0.042)及多发伤(p = 0.043)、脑水肿(p = 0.007),高血糖(p = 0.042)、高乳酸血症(p = 0.029)均与出院时脑功能不良相关。Logistic回归分析显示,入院时的高BIG评分是出院时脑功能不良的独立危险因素。ROC曲线分析确定的最佳BIG评分阈值为17.5,以此预测不良预后的敏感性为66.7%,特异性为88.0%。结论:接受DC的中重度TBI患儿出院时的总体脑功能不良比例为54.5%。入院时的BIG评分能够预测这些患儿出院时的早期脑功能预后,具有较高的敏感性和特异性。

基于IDEAS模式的术前访视联合共情支持对严重烧伤患者生理应激、应对方式和瘢痕恢复的影响

目的:基于IDEAS模式(即识别问题、确认选项、评估并选择最佳选项、行动和检验有效性)的术前访视联合共情支持对严重烧伤患者生理应激、应对方式和瘢痕恢复的影响。方法:选取2021年2月-2023年2月笔者科室收治的90例严重烧伤患者为研究对象,按照随机数字表法分为观察组和对照组,各45例。对照组给予共情支持,观察组给予基于IDEAS模式的术前访视护理联合共情支持,比较两组生理应激[去甲肾上腺素(Norepinephrine,NE)、血管紧张素Ⅱ(Angiotensin Ⅱ,AngⅡ)水平]、心理状况[采用汉密尔顿焦虑量表(Hamilton anxiety scale,HAMA)和汉密尔顿抑郁量表(Hamilton depression scale,HAMD)评估]、应对方式[采用医学应对问卷(Medical coping modes questionnaire,MCMQ)评估]、自理能力[采用自我护理能力量表(Exercise of self-care agency scale,ESCA)]和瘢痕恢复情况[采用温哥华瘢痕量表(Vancouver scar scale,VSS)评估]。结果:干预后,观察组血清NE、AngⅡ水平低于对照组,HAMA、HAMD评分低于对照组,MCMQ评分优于对照组,ESCA各维度评分高于对照组,VSS评分及总分低于对照组,差异均有统计学意义(P<0.05)。结论:基于IDEAS模式的术前访视联合共情支持可有效减轻严重烧伤患者生理应激,改善不良心理和应对方式,提高自我护理能力,进而促进瘢痕快速恢复。

Big Data Access Control Mechanism Based on Two-Layer Permission Decision Structure

Big data resources are characterized by large scale, wide sources, and strong dynamics. Existing access controlmechanisms based on manual policy formulation by security experts suffer from drawbacks such as low policymanagement efficiency and difficulty in accurately describing the access control policy. To overcome theseproblems, this paper proposes a big data access control mechanism based on a two-layer permission decisionstructure. This mechanism extends the attribute-based access control (ABAC) model. Business attributes areintroduced in the ABAC model as business constraints between entities. The proposed mechanism implementsa two-layer permission decision structure composed of the inherent attributes of access control entities and thebusiness attributes, which constitute the general permission decision algorithm based on logical calculation andthe business permission decision algorithm based on a bi-directional long short-term memory (BiLSTM) neuralnetwork, respectively. The general permission decision algorithm is used to implement accurate policy decisions,while the business permission decision algorithm implements fuzzy decisions based on the business constraints.The BiLSTM neural network is used to calculate the similarity of the business attributes to realize intelligent,adaptive, and efficient access control permission decisions. Through the two-layer permission decision structure,the complex and diverse big data access control management requirements can be satisfied by considering thesecurity and availability of resources. Experimental results show that the proposed mechanism is effective andreliable. In summary, it can efficiently support the secure sharing of big data resources.

网络关系视角下的用户创新生存影响因素分析——以LEGO Ideas为例

以LEGO Ideas作为数据来源,在社交网络平台的相关研究基础上,构建出作者网络和内容网络,并计算出用户创新节点在网络中的中心性。采用生存分析法,将用户创新的“存活”视为生存事件,建立生存回归模型,分阶段计算出网络中心性对用户创新生存的影响系数。研究认为,两类网络属性在用户创新发布早期为其生存提供正向影响,作者网络的影响在后期愈发明显,而内容网络则逐渐出现负向影响的迹象。企业应鼓励OI平台中高质量作者间的互动,更多地根据作者行为识别出高质量的用户创新。

Leveraging the potential of big genomic and phenotypic data for genome-wide association mapping in wheat

Genome-wide association mapping studies(GWAS)based on Big Data are a potential approach to improve marker-assisted selection in plant breeding.The number of available phenotypic and genomic data sets in which medium-sized populations of several hundred individuals have been studied is rapidly increasing.Combining these data and using them in GWAS could increase both the power of QTL discovery and the accuracy of estimation of underlying genetic effects,but is hindered by data heterogeneity and lack of interoperability.In this study,we used genomic and phenotypic data sets,focusing on Central European winter wheat populations evaluated for heading date.We explored strategies for integrating these data and subsequently the resulting potential for GWAS.Establishing interoperability between data sets was greatly aided by some overlapping genotypes and a linear relationship between the different phenotyping protocols,resulting in high quality integrated phenotypic data.In this context,genomic prediction proved to be a suitable tool to study relevance of interactions between genotypes and experimental series,which was low in our case.Contrary to expectations,fewer associations between markers and traits were found in the larger combined data than in the individual experimental series.However,the predictive power based on the marker-trait associations of the integrated data set was higher across data sets.Therefore,the results show that the integration of medium-sized to Big Data is an approach to increase the power to detect QTL in GWAS.The results encourage further efforts to standardize and share data in the plant breeding community.

Big Data Application Simulation Platform Design for Onboard Distributed Processing of LEO Mega-Constellation Networks

Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In existing technologies,the efficiency of big data applications(BDAs)in distributed systems hinges on the stable-state and low-latency links between worker nodes.However,LMCNs with high-dynamic nodes and long-distance links can not provide the above conditions,which makes the performance of OBDP hard to be intuitively measured.To bridge this gap,a multidimensional simulation platform is indispensable that can simulate the network environment of LMCNs and put BDAs in it for performance testing.Using STK's APIs and parallel computing framework,we achieve real-time simulation for thousands of satellite nodes,which are mapped as application nodes through software defined network(SDN)and container technologies.We elaborate the architecture and mechanism of the simulation platform,and take the Starlink and Hadoop as realistic examples for simulations.The results indicate that LMCNs have dynamic end-to-end latency which fluctuates periodically with the constellation movement.Compared to ground data center networks(GDCNs),LMCNs deteriorate the computing and storage job throughput,which can be alleviated by the utilization of erasure codes and data flow scheduling of worker nodes.

An Innovative K-Anonymity Privacy-Preserving Algorithm to Improve Data Availability in the Context of Big Data

The development of technologies such as big data and blockchain has brought convenience to life,but at the same time,privacy and security issues are becoming more and more prominent.The K-anonymity algorithm is an effective and low computational complexity privacy-preserving algorithm that can safeguard users’privacy by anonymizing big data.However,the algorithm currently suffers from the problem of focusing only on improving user privacy while ignoring data availability.In addition,ignoring the impact of quasi-identified attributes on sensitive attributes causes the usability of the processed data on statistical analysis to be reduced.Based on this,we propose a new K-anonymity algorithm to solve the privacy security problem in the context of big data,while guaranteeing improved data usability.Specifically,we construct a new information loss function based on the information quantity theory.Considering that different quasi-identification attributes have different impacts on sensitive attributes,we set weights for each quasi-identification attribute when designing the information loss function.In addition,to reduce information loss,we improve K-anonymity in two ways.First,we make the loss of information smaller than in the original table while guaranteeing privacy based on common artificial intelligence algorithms,i.e.,greedy algorithm and 2-means clustering algorithm.In addition,we improve the 2-means clustering algorithm by designing a mean-center method to select the initial center of mass.Meanwhile,we design the K-anonymity algorithm of this scheme based on the constructed information loss function,the improved 2-means clustering algorithm,and the greedy algorithm,which reduces the information loss.Finally,we experimentally demonstrate the effectiveness of the algorithm in improving the effect of 2-means clustering and reducing information loss.

Exploring impacts of COVID-19 on spatial and temporal patterns of visitors to Canadian Rocky Mountain National Parks from social media big data

COVID-19 posed challenges for global tourism management.Changes in visitor temporal and spatial patterns and their associated determinants pre-and peri-pandemic in Canadian Rocky Mountain National Parks are analyzed.Data was collected through social media programming and analyzed using spatiotemporal analysis and a geographically weighted regression(GWR)model.Results highlight that COVID-19 significantly changed park visitation patterns.Visitors tended to explore more remote areas peri-pandemic.The GWR model also indicated distance to nearby trails was a significant influence on visitor density.Our results indicate that the pandemic influenced tourism temporal and spatial imbalance.This research presents a novel approach using combined social media big data which can be extended to the field of tourism management,and has important implications to manage visitor patterns and to allocate resources efficiently to satisfy multiple objectives of park management.

The Impact of Big Five Personality Traits on Older Europeans’ Physical Health

Investigating the role of Big Five personality traits in relation to various health outcomes has been extensively studied. The impact of “Big Five” on physical health is here explored for older Europeans with a focus on examining age groups differences. The study sample included 378,500 respondents derived from the seventh data wave of Survey of Health, Aging and Retirement in Europe (SHARE). The physical health status of older Europeans was estimated by constructing an index considering the combined effect of well-established health indicators such as the number of chronic diseases, mobility limitations, limitations with basic and instrumental activities of daily living, and self-perceived health. This index was used for an overall physical health assessment, for which the higher the score for an individual, the worst health level. Then, through a dichotomization process applied to the retrieved Principal Component Analysis scores, a two-group discrimination (good or bad health status) of SHARE participants was obtained as regards their physical health condition, allowing for further con-structing logistic regression models to assess the predictive significance of “Big Five” and their protective role for physical health. Results showed that neuroti-cism was the most significant predictor of physical health for all age groups un-der consideration, while extraversion, agreeableness and openness were not found to significantly affect the self-reported physical health levels of midlife adults aged 50 up to 64. Older adults aged 65 up to 79 were more prone to open-ness, whereas the oldest old individuals aged 80 up to 105 were mainly affected by openness and conscientiousness. .

Evaluation of a software positioning tool to support SMEs in adoption of big data analytics

Big data analytics has been widely adopted by large companies to achieve measurable benefits including increased profitability,customer demand forecasting,cheaper development of products,and improved stock control.Small and medium sized enterprises(SMEs)are the backbone of the global economy,comprising of 90%of businesses worldwide.However,only 10%SMEs have adopted big data analytics despite the competitive advantage they could achieve.Previous research has analysed the barriers to adoption and a strategic framework has been developed to help SMEs adopt big data analytics.The framework was converted into a scoring tool which has been applied to multiple case studies of SMEs in the UK.This paper documents the process of evaluating the framework based on the structured feedback from a focus group composed of experienced practitioners.The results of the evaluation are presented with a discussion on the results,and the paper concludes with recommendations to improve the scoring tool based on the proposed framework.The research demonstrates that this positioning tool is beneficial for SMEs to achieve competitive advantages by increasing the application of business intelligence and big data analytics.

滴滴快车：“故障”广告其实是Big Idea

滴滴快车最新广告片在播放时出现了“故障”，部分画面的呈现就像部老旧电视机，时不时出现画面缺失或者卡顿。你以为这是谁的粗心之举，那就错了，相反，这是故意为之。在滴滴快车最近一次进行品牌升级的传播活动中，这则名为《想出发就出发》的视频成为了宣传主体，而看似“故障”的拍摄手法其实是滴滴快车想借用艺术特效，用抹掉删除的视觉效果，让同一句文案传递出不同的两种人生态度。

Big data challenge for monitoring quality in higher education institutions using business intelligence dashboards

As big data becomes an apparent challenge to handle when building a business intelligence(BI)system,there is a motivation to handle this challenging issue in higher education institutions(HEIs).Monitoring quality in HEIs encompasses handling huge amounts of data coming from different sources.This paper reviews big data and analyses the cases from the literature regarding quality assurance(QA)in HEIs.It also outlines a framework that can address the big data challenge in HEIs to handle QA monitoring using BI dashboards and a prototype dashboard is presented in this paper.The dashboard was developed using a utilisation tool to monitor QA in HEIs to provide visual representations of big data.The prototype dashboard enables stakeholders to monitor compliance with QA standards while addressing the big data challenge associated with the substantial volume of data managed by HEIs’QA systems.This paper also outlines how the developed system integrates big data from social media into the monitoring dashboard.