氮酮与薄荷醇联用对5-氟尿嘧啶的促透作用

目的：以5-氟尿嘧啶（5-FU）为模型药物，对氮酮和薄荷醇的促透特性进行比较，并探讨两种促透剂合用时的促透效果。方法：在离体透皮实验装置上进行透皮试验，由浓度计算累积透过量，运用倍量法对氮酮与薄荷醇联用的效果进行评价。结果： 浓度为0.25%、0.50%、1.00%、2.00%的氮酮对5-FU均有促透作用，与空白对照组相比，差异具有统计学意义（P<0.01）；浓度为0.25%、0.50%、1.00%、2.00%的薄荷醇对5-FU也有明显的促透作用，与空白对照相比，差异具有统计学意义（P<0.01）；用倍量法对两种药物联用的作用进行评价分析时，二者联用未显示有协同作用。结论：氮酮和薄荷醇对（5-FU）均有促透作用，但二者以相同浓度合用时，未有明显的协同作用。     

An Integrated Flexible All-Nanowire Infrared Sensing System with Record Photosensitivity

Infrared (IR) photodetectors are a key optoelectronic device and have thus attracted considerable research attention in recent years. Photosensitivity is an increasingly important device performance parameter for nanoscale photodetectors and image sensors, as it determines the ultimate imaging quality and contrast. However, photosensitivities of state-of-the-art low-dimensional nanostructure-based IR detectors are considerably low, limiting their practical applications. Herein, a biomimetic IR detection amplification (IRDA) system that boosts photosensitivity by several orders of magnitude by introducting nanowire field effect transistors (FETs), resulting in a peak photosensitivity of 7.6 × 10⁴ under an illumination of 1342 nm, is presented. Consequently, high-contrast imaging of IR light is obtained on the flexible IRDA arrays. The image information can be then trained and recognized by an artificial neural network for higher image-recognition efficiency. This work provides a new perspective for developing high-performance IR imaging systems, and is expected to undoubtedly enlighten future work on artificial intelligence and biorobotic systems.     

ZerNet: Convolutional Neural Networks on Arbitrary Surfaces Via Zernike Local Tangent Space Estimation

In this paper, we propose a novel formulation extending convolutional neural networks (CNN) to arbitrary two-dimensional manifolds using orthogonal basis functions called Zernike polynomials. In many areas, geometric features play a key role in understanding scientific trends and phenomena, where accurate numerical quantification of geometric features is critical. Recently, CNNs have demonstrated a substantial improvement in extracting and codifying geometric features. However, the progress is mostly centred around computer vision and its applications where an inherent grid-like data representation is naturally present. In contrast, many geometry processing problems deal with curved surfaces and the application of CNNs is not trivial due to the lack of canonical grid-like representation, the absence of globally consistent orientation and the incompatible local discretizations. In this paper, we show that the Zernike polynomials allow rigourous yet practical mathematical generalization of CNNs to arbitrary surfaces. We prove that the convolution of two functions can be represented as a simple dot product between Zernike coefficients and the rotation of a convolution kernel is essentially a set of 2 × 2 rotation matrices applied to the coefficients. The key contribution of this work is in such a computationally efficient but rigorous generalization of the major CNN building blocks.     

基于信息熵与形态学的鸟鸣数据处理

为了探索一种减少训练量并提高精度,且适应于卷积神经网络的预处理方法,以识别鸟鸣为例,基于信息熵以及形态学在图像处理上的应用,根据鸟鸣的间隔性特点,提出新的处理方案。利用形态学将音频片段归类为有效和噪声信号两类;使用加权的方法利用信息熵预测有效数据的分布。仿真结果表明,形态学使数据量减少且信息熵处理使单个数据稀疏化并起到滤波作用,在保持精度甚至精度提高的情况下缩短了训练时间,为卷积神经网络的数据预处理的简化提供了方向。     

Action-research-based optimisation model for health care behaviour change in rural India

In this research, we utilise a multi-method braided approach for a real-world health care intervention in rural India. We first use action research to conceptually underpin the role of self-help groups, disease profiles and appropriate interventions in health care behaviour change and improvement. Action research is then coupled with a non-linear integer-programming-based simulation to determine how health care resources need to be distributed over time, disease type and groups. The action research provides specific research questions and distributional forms needed for the optimisation analysis, while the non-linear integer-programming simulation provides specific recommendations on where and when to distribute resources. External validity of the study is ensured by the adoption of recommendations in the field as part of the action research. Findings from this research have important policy implications. For example, it identifies the value of continuous embedded interventions as opposed to periodic ones and the importance of flexibility in terms of which community groups and disease types to serve over time. This research also provides an important bridge between the operations research, health care in developing nations, organisational theory and humanitarian operations bodies of knowledge.     

Recognition of occupational therapy exercises and detection of compensation mistakes for Cerebral Palsy

Depth camera-based virtual rehabilitation systems are gaining attention in occupational therapy for cerebral palsy patients. When developing such a system, domain-specific exercise recognition is vital. To design such a gesture recognition method, some obstacles need to be overcome: detection of gestures not related to the defined exercise set and recognition of incorrect exercises performed by the patients to compensate for their lack of ability. We propose a framework based on hidden Markov models for the recognition of upper extremity functional exercises. We determine critical compensation mistakes together with restrictions for classifying these mistakes with the help of occupational therapists. We first eliminate undefined gestures by evaluating two models that produce adaptive threshold values. Then we utilize specific negative models based on feature thresholding and train them for each exercise to detect compensation mistakes. We perform various tests using our method in a laboratory environment under the supervision of occupational therapists.     

多线激光V型焊缝图像特征提取与轨迹识别

为了减少焊缝识别中传感器安装位置带来的前置误差，提高识别精度，设计了一款多线激光传感器，对多线激光在V型焊缝上的图像进行处理得到焊缝轨迹。提出一种基于动态不规则感兴趣区域的图像分割方法，将多条激光分割后并行处理。单条激光线图像预处理后以像素行灰度值之和的极大值确定激光条纹所在行，对激光条纹分段拟合后求交点，得到坡口边缘与焊缝位置的特征点，并将焊缝位置特征点拟合得到焊缝轨迹。根据所提出的算法编写了焊缝轨迹识别程序，并进行实验，结果表明，所提出的算法识别焊缝走向误差在0.5°之内，符合焊缝跟踪的要求。     

Human uncertainty in explicit user feedback and its impact on the comparative evaluations of accurate prediction and personalisation

ABSTRACT

In this article, we report on the lack of reliability of explicit user feedback and its interpretation in the light of system evaluation. It is known, that given feedback strongly depends on the situational context. But also when many contextual factors are held constant, user feedback still proves to be unreliable. This impacts the evaluation of predictive algorithms since it is not clear whether a deviation between a user response and its corresponding prediction can be seen as a flaw by the system or just as usual ‘human uncertainty’. As a result, the perspective on the evaluation of adaptive systems basically changes. The main goal of this article is to demonstrate that simply increasing the amount of explicit feedback is not the key to sustainable system design innovation, as long as that information is not appropriately evaluated. To this end, we will exploit a novel probabilistic approach of processing user feedback and identify biasing effects on accuracy metrics, error probabilities for system rankings as well as natural limitations of evaluation. Finally, we will discuss possible solution strategies and give advice for handling explicit user feedback that is associated with uncertainty.     

A hybrid artificial bee colony algorithm for flexible job shop scheduling with worker flexibility

The traditional flexible job shop scheduling problem (FJSP) considers machine flexibility but not worker flexibility. Given the influence and potential of human factors in improving production efficiency and decreasing the cost in practical production systems, we propose a mathematical model of an extended FJSP with worker flexibility (FJSPW). A hybrid artificial bee colony algorithm (HABCA) is presented to solve the proposed FJSPW. For the HABCA, effective encoding, decoding, crossover and mutation operators are designed, and a new effective local search method is developed to improve the speed and exploitation ability of the algorithm. The Taguchi method of Design of Experiments is used to obtain the best combination of key parameters of the HABCA. Extensive computational experiments carried out to compare the HABCA with some well-performing algorithms from the literature confirm that the proposed HABCA is more effective than these algorithms, especially on large-scale FJSPW instances.