Operating room black box:Scrutinizer of theatre practices

Objective:Adverse surgical events are a major cause of morbidity,mortality,and disability worldw ide.The cause of many such events can be attributed to interruptions in the operating room(OR),muli-tasking by surgeons,etc.The objective of this study was to observe the types and frequency of intra-operative wor kflow interruptions in our ORs.Method:This ccoss-sectional study was conducted from March Do April of 2023.An observational approach using an audio-video recording device was employed to record OR flow disr uptions.One elective OR and one emergency OR under the Department of General Surgery were selected for the study.All open and laparoscopic surger ies conducted in the selected ORs were included.An Internet Protocol camera'was installed in the selec ted ORS with a view of the entire room,including the anesthesia station.Audio-video recording was started after the first indsion and stopped after closure of the surgical site.Result:Of the 51 cases that were studied,45(88.2%)were elective,and 18(35.3%)were laparoscopic cases.They could be classified into 8 types of open procedures and 4 types of lapar oscopic procedures.The mean maximum headcount inside the OR was 15.5土3.6 and doors opened on average of 15.8土6.0 times during a procedure.Other interruptions were surgeons attending phone calls(24,47.1%),leaving the sterile area(21,41.2%),technical disturbances(32,62.7%),anesthetic interruptions(18,35.3%),and faulty instruments(29,56.9%)Elective procedures had a signifcandy higher average number of in-terruptions per operating hour than emergency procedures(175±8.6vs.7.1±2.9,p<0.01).Condusion:Preventable factors such as faulty instruments,anesthetic interruption,and attending phone calls by the surgeon are commonly observed in ORs.They need to be addressed by timely surgical audits or the adoption of continued sureillance methods that can help take measures to minimize their occurrence.

On the Effectiveness of the System Validation Based on the Black Box Testing Methodology

With the advancement of technology in recent years, effective fault diagnosis became a necessity to verify the performance and ensure the quality of complex systems. In this paper, an original verification methodology for complex consumer electronic devices is presented. Verification of the system which consists of hardware （integrated circuit） and corresponding software within a flat panel TV set is in the focus. Proposed methodology provides reliable functional failure detection using the concept of black box testing. Further, the approach is fully automated, improving the reliability and speed of failure detection. The methodology effectiveness has been experimentally evaluated and the analysis results have been reported.

Analysis of Data on Xanthan Fermentation in Stationary Phase Using Black Box and Metabolic Network Models

The xanthan fermentation data in the stationary phase was analyzed using the black box and the metabolic network models. The data consistency is checked through the elemental balance in the black box model. In the metabolic network model, the metabolic flux distribution in the cell is calculated using the metabolic flux analysis method, then the maintenance coefficients is calculated.

The Human Eye Pupil Detection System Using BAT Optimized Deep Learning Architecture

The pupil recognition method is helpful in many real-time systems,including ophthalmology testing devices,wheelchair assistance,and so on.The pupil detection system is a very difficult process in a wide range of datasets due to problems caused by varying pupil size,occlusion of eyelids,and eyelashes.Deep Convolutional Neural Networks(DCNN)are being used in pupil recognition systems and have shown promising results in terms of accuracy.To improve accuracy and cope with larger datasets,this research work proposes BOC(BAT Optimized CNN)-IrisNet,which consists of optimizing input weights and hidden layers of DCNN using the evolutionary BAT algorithm to efficiently find the human eye pupil region.The proposed method is based on very deep architecture and many tricks from recently developed popular CNNs.Experiment results show that the BOC-IrisNet proposal can efficiently model iris microstructures and provides a stable discriminating iris representation that is lightweight,easy to implement,and of cutting-edge accuracy.Finally,the region-based black box method for determining pupil center coordinates was introduced.The proposed architecture was tested using various IRIS databases,including the CASIA(Chinese academy of the scientific research institute of automation)Iris V4 dataset,which has 99.5%sensitivity and 99.75%accuracy,and the IIT(Indian Institute of Technology)Delhi dataset,which has 99.35%specificity and MMU(Multimedia University)99.45%accuracy,which is higher than the existing architectures.

Black Box Modeling Method and Its Application in Refractories Techniques

Concept of black box, condition of modeling and control methods were introduced. Taking high purity silica brick as an example, the silica brick featured with high purity, low porosity, low true density and high strength was developed through problem analysis, test design, modeling and optimization. Using information technology to upgrade refractories industry was discussed.

Electric power generation technology of natural gas pressure reduction:Insights from black box-gray box hierarchical exergy analysis and evaluation method

Based on the“three box”exergy analysis model,a black box-gray box hierarchical exergy analysis and evaluation method is put forward in this paper,which is applied to evaluate the power generation technology of differential pressure produced by natural gas expansion.By using the exergy analysis theory,the black box-gray box hierarchical exergy analysis models of three differential pressure power generation technologies are established respectively.Firstly,the“black box”analysis models of main energy consuming equipment are established,and then the“gray box”analysis model of the total system is established.Based on the calculation results of exergy analysis indexes,the weak energy consumption equipment in the whole power generation process is accurately located.Taking a gas field in southwest China as an example,the comprehensive energy consumption evaluation of the three power generation technologies is carried out,and the technology with the best energy consumption condition among the three technologies is determined.Finally,the rationalization improvement measures are put forward from improving the air tightness,replacing the deflector and reducing the flow loss.

Cultivation of Modern and New Farmers from the Perspective of Black Box of Science and Technology

On the basis of analyzing the connotation and function of black box of science and technology,this article expounds the necessity of cultivating modern and new farmers in China at present,and points out that with incessant progress of science and technology,the modern agriculture based on black box of science and technology will continue to grow,which requires a large number of new farmers who can learn and improve black box of agricultural science and technology.Finally,the recommendations are put forward for cultivation of new farmers:improving farmers' training system;enhancing rural financial support,so that the farmers benefit from black box of science and technology;strengthening the cultivation of the practical ability,and promoting farmers' management capacity;strengthening the cultivation of innovative ability,and nurturing innovative farmers.

Exploring Image Culture Through Narrative： A Study on Jennifer Egan＇s Twitter Fiction Black Box

Notable for the completeness with which it surrenders formally and artistically to the textual dictates of Twitter, Jennifer Egan＇s 2012 short science fiction Black Box is one of the most triumphant and fully-fledged fictions written in the form of new media. This paper explores the Twitter narrative employed in Black Box, pointing out that the serialized tweeting format of the story released via computer, mobile phone, or other electronic equipments brings readers immediate reading experience, allowing readers to sense the same feelings as the protagonist does. Through the experimental serialization of＂Twitter＂ narrative, Egan expresses her concerns and worries about the security of the American security as well as the whole world in the post-＂9.11＂ period and at the same time she embraces the virtues and pleasures of traditional storytelling delivered through a wholly new digital format. This paper concludes that Black Box is perhaps one of the boldest experiments of narrative form and is direct exploration into the contemporary image culture.

黑盒与逆向工程概念在数字电路基础实验教学中的应用

随着电子信息技术的飞速发展,传统数字电路实验内容虽较为经典,其授课模式难以真实反映学生实验完成情况,难以有效促进学生对理论课程知识的深入理解。对此摒弃传统FPGA黑盒实验系统中的正向验证方式,采取逆向分析方法,通过对电路外特性测试,进行电路功能判断,强化学生数字电路理论知识,提高学生逆向工程、自主设计和工程应用能力。实验设计符合工程教育认证标准,提高数字电路实验教学质量。

并网变流器全工况阻抗模型的黑箱辨识方法

阻抗分析法因适用于并网变流器控制结构和参数均未知的黑箱系统,已成为工程中评估宽频振荡风险的重要选择。以跟网型三相两电平变流器为例,首先,使用谐波线性化方法,严格推导了计及直流侧耦合的交流侧全工况开/闭环理论阻抗模型,实现了模型参数与工作点高阶项的完全解耦。其次,依据理论模型特征,明确了黑箱辨识方法的基本原理,分析了辨识单一开/闭环阻抗模型频率响应所需的工况数量。使用辨识得到的全工况阻抗模型,实现了不同短路比条件下系统安全运行域的快速评估,并通过时域仿真验证了其准确性。此外,讨论了所提方法的合理性、普适性以及相对于深度学习方法的优越性,为阻抗分析法的实际应用提供了参考。

DCVAE-adv:A Universal Adversarial Example Generation Method for White and Black Box Attacks

Deep neural network(DNN)has strong representation learning ability,but it is vulnerable and easy to be fooled by adversarial examples.In order to handle the vulnerability of DNN,many methods have been proposed.The general idea of existing methods is to reduce the chance of DNN models being fooled by observing some designed adversarial examples,which are generated by adding perturbations to the original images.In this paper,we propose a novel adversarial example generation method,called DCVAE-adv.Different from the existing methods,DCVAE-adv constructs adversarial examples by mixing both explicit and implicit perturbations without using original images.Furthermore,the proposed method can be applied to both white box and black box attacks.In addition,in the inference stage,the adversarial examples can be generated without loading the original images into memory,which greatly reduces the memory overhead.We compared DCVAE-adv with three most advanced adversarial attack algorithms:FGSM,AdvGAN,and AdvGAN++.The experimental results demonstrate that DCVAE-adv is superior to these state-of-the-art methods in terms of attack success rate and transfer ability for targeted attack.Our code is available at https://github.com/xzforeverlove/DCVAE-adv.

An Efficient Method to Estimate Admittance of Black-boxed Inverter-based Resources for Varying Operating Points

Traditional analytical approaches for stability assessment of inverter-based resources(IBRs),often requiring detailed knowledge of IBR internals,become impractical due to IBRs’proprietary nature.Admittance measurements,relying on electromagnetic transient simulation or laboratory settings,are not only time-intensive but also operationally inflexible,since various non-linear control loops make IBRs’admittance models operating-point dependent.Therefore,such admittance measurements must be performed repeatedly when operating point changes.To avoid time-consuming and cumbersome measurements,admittance estimation for arbitrary operating points is highly desirable.However,existing admittance estimation algorithms usually face challenges in versatility,data demands,and accuracy.Addressing this challenge,this letter presents a simple and efficient admittance estimation method for blackboxed IBRs,by utilizing a minimal set of seven operating points to solve a homogeneous linear equation system.Case studies demonstrate this proposed method ensures high accuracy across various types of IBRs.Estimation accuracy is satisfying even when non-negligible measurement errors exist.

MarkNeRF:Watermarking for Neural Radiance Field

This paper presents a novel watermarking scheme designed to address the copyright protection challenges encountered with Neural radiation field(NeRF)models.We employ an embedding network to integrate the watermark into the images within the training set.Then,theNeRFmodel is utilized for 3Dmodeling.For copyright verification,a secret image is generated by inputting a confidential viewpoint into NeRF.On this basis,design an extraction network to extract embedded watermark images fromconfidential viewpoints.In the event of suspicion regarding the unauthorized usage of NeRF in a black-box scenario,the verifier can extract the watermark from the confidential viewpoint to authenticate the model’s copyright.The experimental results demonstrate not only the production of visually appealing watermarks but also robust resistance against various types of noise attacks,thereby substantiating the effectiveness of our approach in safeguarding NeRF.

Medical artificial intelligence and the black box problem:a view based on the ethical principle of“do no harm”

One concern about the application of medical artificial intelligence(AI)regards the“black box”feature which can only be viewed in terms of itsinputs and outputs,with no way to understand the AI’s algorithm.Thisis problematic because patients,physicians,and even designers,do not understand why or how a treatment recommendation is produced by AI technologies.One view claims that the worry about black-box medicine is unreasonable because AI systems outperform human doctors in identifying the disease.Furthermore,under the medical AI-physicianpatient model,the physician can undertake the responsibility of interpreting the medical AI’s diagnosis.In this study,we focus on the potential harm caused by the unexplainability feature of medical AI and try to show that such possible harm is underestimated.We will seek to contribute to the literature from three aspects.First,we appealed to a thought experiment to show that although the medical AI systems perform better on accuracy,the harm caused by medical AI’s misdiagnoses may be more serious than that caused by human doctors’misdiagnoses in some cases.Second,in patient-centered medicine,physicians were obligated to provide adequate information to their patients in medical decision-making.However,the unexplainability feature of medical AI systems would limit the patient’s autonomy.Last,we tried to illustrate the psychological and financial burdens that may be caused by the unexplainablity feature of medical AI systems,which seems to be ignored by the previous ethical discussions.

神经网络类机理建模下的持续自学习控制

针对未知、时变复杂动力学系统在基于模型的控制中的动态建模问题,本文采用前向全连接神经网络对动力学系统进行数据驱动下的非机理拟合建模.通过动态线性化和归一化/反归一化数据处理,基于前向传播算法,将神经网络的网络拓扑计算过程转化成动力学系统机理模型的同构等价表达形式.与基于模型的预测与反演控制相结合,提出了神经网络类机理建模下的持续自学习控制方法,探索了神经网络在动力学系统建模与控制中的可解释性问题.以机械臂为控制对象的仿真结果表明,神经网络类机理模型与机理模型在形式上同构,在参数上近似或等价,可用于控制系统控制品质的定性、定量分析.持续自学习控制对非线性未知、时变复杂系统具有较好的动态适应能力.

金融强国目标下数字金融平台算法黑箱的系统治理机制

在金融强国目标与人工智能技术的驱动下,数字金融平台快速兴起并将算法技术深度应用于传统金融行业。数字金融平台是数字金融与平台经济的融合产物,其通过输出技术或提供场景与金融机构开展合作活动,具有天然的技术偏好与金融属性,有效促进金融产品与服务的数字化与普惠性。然而,任何事物都具有双面性,不是非此即彼的结果,而是利弊同在的产物。通过采用案例分析方法发现,算法技术深度应用于数字金融平台总是存在着一定的误差或测不准(算法黑箱),具有不同于传统企业的太大不能倒、太多不能倒、太强不能倒的系统性风险特征,并在三方系统(平台端、用户端与监管端)形成了算法黑箱风险:算法黑箱具有平台迷雾风险,阻碍平台端的稳定发展;算法黑箱具有技术遮蔽风险,加剧用户端的风险缺陷;算法黑箱具有监管真空风险,制约监管端的公权治理。因此,为实现金融强国的目标,防范化解数字金融平台算法黑箱的系统性风险,基于三方控制理论与国际算法黑箱风险的系统治理经验,我国应当从三方系统(平台端、用户端、监管端)构建数字金融平台算法黑箱的系统治理机制。

基于BI的大型公立医院绩效管理系统设计与应用

为了提升医院绩效考核管理效率,基于商业智能(BI)技术,构建区域大型公立医院绩效管理系统。系统包括商业智能分析子系统、Web应用过程子系统和后台数据库三个部分。采用成熟的Windows操作系统、Java EE开发技术,后台数据库采用Oracle OBIEE平台,确保系统运行的稳定性;系统软件基于B/S构建了表示层、业务逻辑层、数据层三层架构,确保了系统简单易操作,同时受众面广等特点;系统数据库采用事实星座模式进行构建,确保系统具备高速运行速度和升级扩展影响性。通过商业智能技术与Web应用过程子系统的完美衔接,构建起医院绩效分析平台。该平台可实现绩效指标查询、提取、打印等6大功能。通过黑盒测试法和Load Runner测试软件对系统功能和性能进行了测试,结果表明,系统各项功能均符合预期,系统响应时间均控制在3 s以内。

失事目标水中搜寻技术发展现状及趋势

针对近年来海洋事故日益增多的问题,本文研究分析了水中搜寻技术发展现状及趋势。本文以水中搜寻技术为对象,对当前水中失事目标进行了分类,分析了水中搜救策略研究现状,讨论了3种数据与模型搜寻方法。阐述了水中搜寻平台与设备发展情况,结合当前搜寻技术的不足,从增强失事目标定位能力,提高水下机器人水下搜寻能力与智能化,提高集群化、多平台协同组网能力和提高声学探测设备性能与数据处理能力4个方面给出了水中搜寻技术发展趋势。

基于ν-支持向量机的波浪中船舶操纵运动辨识建模

本文采用系统辨识方法对船舶在波浪中的三自由度操纵运动进行黑箱建模。以KCS船为研究对象,建模所需的数据由规则波中的船模回转试验获得。分别利用原始数据及经低通滤波处理后的数据,应用ν-支持向量机进行黑箱建模。进一步地,利用所建立的模型进行运动预报,并将预报结果与真实的试验值进行比较,对所提出的建模方法进行验证。结果表明,ν-支持向量机是一种可进行波浪中的船舶操纵运动辨识建模的有效方法。

用黑盒电弧模型的爆炸开关中直流电弧的仿真

爆炸开关是一种由炸药驱动、快速响应、高可靠性的直流断路器,已在ITER、EAST等多种失超保护系统中得到应用。作为一种直流断路器,电弧是决定其换流过程的主要因素,其水下燃弧、熄弧的特性的相关研究是目前此类断路器设计中的空白。电弧动态模型是研究电弧特性的常用方法,当建立的模型与试验过程中所观察到的断路器性能有良好的对应关系时,模型可用于建立爆炸开关的结构设计和换相特性之间的关系,为开关的设计优化提供理论依据。通过分析对比了几种不同的电弧模型,给出了适用于爆炸开关设计过程中的黑盒电弧模型,应用了Parameter Sweeping Strategy,研究了电弧模型中的参数取值规律,并结合试验验证了这种模型的可行性,为中国核聚变工程试验堆(CFETR)失超保护系统中爆炸开关中的研制提供了理论依据。