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.

Application of ″Black Box″ to fault diagnosis of rotating machine

Presents the successful application of an accident recalling system in the Linyuan refine oil works as part of a rotating machine vibration state monitoring and fault diagnosis system which consists of vibration pre processor,comparator and plus generator, and system gives the CPU of vibration state monitoring and fault diagnose system an interrupt plus when the vibration amplitude exceed a dangerous level to enable it to sample and store the vibration data and gets the accident data timely because the interval between the happening of accident and the beginning of sampling are shorter than 1 ms.

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.

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.

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.

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 ls 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.

A Novel Black Box Based Behavioral Model of Power Amplifier for WCDMA Applications

In this paper, Black Box approach is presented for behavioral modeling of a non linear power amplifier with memory effects. Large signal parameters of a Motorola LDMOS power amplifier driven by a WCDMA signal were extracted while taking into considerations the power amplifier’s bandwidth. The proposed model was validated based on the simulated data. Some validation results are presented both in the time and frequency domains, using WCDMA signal.

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.

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.

Box-Behnken响应面法结合BP神经网络优化黑胡椒脐贴处方研究

目的采用Box-Behnken响应面法结合BP神经网络优化黑胡椒脐贴处方。方法在前期预试验的基础上,以载药量、促渗剂用量、聚乙二醇(polyethylene glycol,PEG)6000与微粉硅胶的比例为影响因素,进行单因素试验。结合体外透皮试验,利用高效液相色谱法测定胡椒碱的含量,对胡椒碱峰面积、指纹图谱的总峰面积和脐贴的成型性、剥离残留度和基质填充情况赋以权重,所得结果为综合评价指标。在单因素试验的基础上利用Box-Behnken响应面法结合BP神经网络优化脐贴处方。结果黑胡椒脐贴的最佳处方为载药量7%、氮酮4.5%、PEG6000与微粉硅胶的质量比为43∶1。结论黑胡椒脐贴的处方合理,可为后续贴剂的药效学研究提供基础。

Box-Behnken响应面法优选改良型正骨灵活血止痛膏成型工艺

目的:优选改良型正骨灵活血止痛膏成型工艺。方法:基于Box–Behnken响应面法,以蜂蜡用量、混合温度、料液比为影响因素,以传统经验鉴别法与客观物理参数法的综合评分为评价指标,在单因素试验基础上,考察各因素对综合评分的影响,优选最佳的成型工艺条件。结果:蜂蜡用量为1.9%,混合温度为81℃,料液比为2.5∶1 g/mL。验证试验结果综合评分RSD为0.86%。结论:优选得到的改良型正骨灵活血止痛膏成型工艺稳定可行。

基于Cruise/BlackBox的电动汽车制动能量回收仿真

在使用AVL-Cruise进行整车仿真过程中,需要对制动能量回收策略进行研究。基于AVL-Cruise建立仿真平台,使用Cruise/Black Box引入控制代码,设定一个制动能量回收策略并使用Cruise/Black Box实现控制策略,使用AVL-Cruise联合Cruise/Black Box接口组件对具体的工况进行仿真分析。结果表明,基于Cruise/Black Box的电动汽车制动能量回收仿真,能够完全真实地反映制动能量回收控制策略在电动汽车上的工作过程。

AN ALGORITHM FOR AUTOMATICALLY GENERATING BLACK-BOX TEST CASES

Selection of test cases plays a key role in improving testing efficiency. Black-box testing is an important way of testing, and its validity lies on the selection of test cases in some sense. A reasonable and effective method about the selection and generation of test cases is urgently needed. This letter first introduces some usualmethods on black-box test case generation,then proposes a new algorithm based on interface parameters and discusses its properties, finally shows the effectiveness of the algorithm.

Reliable Space Pursuing for Reliability-based Design Optimization with Black-box Performance Functions

Reliability-based design optimization （RBDO） is intrinsically a double-loop procedure since it involves an overall optimization and an iterative reliability assessment at each search point. Due to the double-loop procedure, the computational expense of RBDO is normally very high. Current RBDO research focuses on problems with explicitly expressed performance functions and readily available gradients. This paper addresses a more challenging type of RBDO problem in which the performance functions are computation intensive. These computation intensive functions are often considered as a ＂black-box＂ and their gradients are not available or not reliable. On the basis of the reliable design space （RDS） concept proposed earlier by the authors, this paper proposes a Reliable Space Pursuing （RSP） approach, in which RDS is first identified and then gradually refined while optimization is performed. It fundamentally avoids the nested optimization and probabilistic assessment loop. Three well known RBDO problems from the literature are used for testing and demonstrating the effectiveness of the proposed RSP method.

A New Black Box Analysis of Hash Functions Based on Block Ciphers

For the 64 most basic ways to construct a hash function H:{0,1} → {0,1}n from a block cipher E:{0,1}n × {0,1}n → {0,1}n, Black et al.provided a formal and quantitative treatment of the 64 constructions, and proved that 20 schemes are collision resistant.This paper improves the upper and lower bounds and make contrast with a hash constructed from a random oracle.These 20 schemes have only one kind of collision resistance upper and lower bounds.In addition, we present new advantages for finding second preimages.

<i>PP</i>and <i>P<span style='text-decoration:overline;'>P</span></i>Multi-Particles Production Investigation Based on CCNN Black-Box Approach

The multiplicity distribution (P(nch)) of charged particles produced in a high energy collision is a key quantity to understand the mechanism of multiparticle production. This paper describes the novel application of an artificial neural network (ANN) black-box modeling approach based on the cascade correlation (CC) algorithm formulated to calculate and predict multiplicity distribution of proton-proton (antiproton) (PP and PP ) inelastic interactions full phase space at a wide range of center-mass of energy . In addition, the formulated cascade correlation neural network (CCNN) model is used to empirically calculate the average multiplicity distribution nch> as a function of . The CCNN model was designed based on available experimental data for = 30.4 GeV, 44.5 GeV, 52.6 GeV, 62.2 GeV, 200 GeV, 300 GeV, 540 GeV, 900 GeV, 1000 GeV, 1800 GeV, and 7 TeV. Our obtained empirical results for P(nch), as well as nch> for (PP and PP) collisions are compared with the corresponding theoretical ones which obtained from other models. This comparison shows a good agreement with the available experimental data (up to 7 TeV) and other theoretical ones. At full large hadron collider (LHC) energy ( = 14 TeV) we have predicted P(nch) and nch> which also, show a good agreement with different theoretical models.

算法解释制度的体系化构建

算法解释在算法治理中举足轻重。算法解释承载着权益保障、社会交往和风险治理三重意义,其在技术层面上的障碍正在逐渐被突破,可以通过多种技术机制实现。在算法治理活动中,应根据常规场景、关键场景和争议场景分别限定选择解释的路径选择和技术方案,通过冻结机制、抽样机制和镜像机制固定算法解释,并使之接受外部的验证与审查,确保算法解释真实、有效。算法解释的系列机制应被进一步构建为体系化的算法解释制度,在这一制度框架内,对解释路径与精度、解释时限、解释瑕疵责任等要素的合理配置,可以实现社会效益与规制负担的精细平衡。

基于损失平滑的对抗样本攻击方法

深度神经网络(DNNs)容易受到对抗样本的攻击,现有基于动量的对抗样本生成方法虽然可以达到接近100%的白盒攻击成功率,但是在攻击其他模型时效果仍不理想,黑盒攻击成功率较低。针对此,提出一种基于损失平滑的对抗样本攻击方法来提高对抗样本的可迁移性。在每一步计算梯度的迭代过程中,不直接使用当前梯度,而是使用局部平均梯度来累积动量,以此来抑制损失函数曲面存在的局部振荡现象,从而稳定更新方向,逃离局部极值点。在ImageNet数据集上的大量实验结果表明:所提方法与现有基于动量的方法相比,在单个模型攻击实验中的平均黑盒攻击成功率分别提升了38.07%和27.77%,在集成模型攻击实验中的平均黑盒攻击成功率分别提升了32.50%和28.63%。