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The target can deceive the flight vehicle by releasing an infrared decoy to make the line-of-sight (LOS) angle rate deflect greatly, thus causing the flight vehicle to miss the target. Therefore, in order to accurately strike the target in complex adversarial scenarios, this paper proposes a finite-time convergence guidance law (FTCG) combined with a finite-time disturbance observer (FTDO). The complex adversarial scenario is established by combining the relative motion model between the flight vehicle and the target and the motion model of the infrared decoy. Based on this, considering the dynamic characteristic of the flight vehicle’s autopilot, a guidance model is obtained. Utilizing sliding mode control theory and finite-time control theory, an FTCG of the LOS angle rate is designed. Then, the finite-time convergence of the guidance law is proved and the total convergence time is derived. Finally, for the target maneuvering that is difficult to measure in the guidance law, an FTDO is used to estimate and compensate for the target maneuvering in the guidance law. Simulation results show that the FTCG can make the LOS angle rate quickly converge and accurately strike the target in different scenarios, with a good guidance accuracy and robustness. Compared with the sliding mode guidance law (SMGL) and the adaptive sliding mode guidance law (ASMGL) based on an extended state observer (ESO), the advantages of the designed guidance law are illustrated. Finally, FTCG is extended to be three dimensional and compared with the proportional navigation guidance law (PNG) to further illustrate its effectiveness in a three-dimensional coordinate system.

Magnetic phenomena are amazing and mysterious topics [...]

The application of a static magnetic field (SMF) to solidification processing has emerged as an advanced strategy for efficiently regulating the macro/micro structures and the mechanical performance of metallic materials. The SMF effects have been proved to be positive in various processes of metal solidification. Firstly, this review briefly introduces two basic magnetic effects, i.e., magnetohydrodynamic effects and magnetization effects, which play crucial roles in regulating metal solidification. Further, the state of the art of solidification processing in the SMF, including undercooling and nucleation, interface energy, grain coarsening and refinement, segregation and porosity, are comprehensively summarized. Finally, the perspective future of taking advantage of the SMF for regulating metal solidification is presented.

In view of the multi-source uncertainty disturbance problem during flight control, the attitude autopilot based on fuzzy linear active disturbance rejection control (F-LADRC) was proposed. A second-order linear active disturbance rejection controller was designed to stabilize the attitude angle of the missiles, and the frequency domain characteristics were analyzed. Firstly, it was proved that the linear expansion state observer (LESO) is convergent and can achieve the indifference estimation of the system state variables and total disturbance. Then, it was proved that the linear active disturbance rejection control (LADRC) possesses disturbance rejection characteristics, and the influence of the bandwidth parameter on the disturbance rejection performance of the system was analyzed. The fuzzy control was used to adjust the parameter adaptively. Finally, the tracking, robustness, and anti-disturbance of the F-LADRC attitude autopilot were verified by performing simulations.

The target spraying effect of spray robots mainly depends on the control performance of the spraying arm during the processes of aiming and tracking. To further improve the robustness of the endpoint control and positioning accuracy of the spray arm, an improved potential field algorithm for the motion planning and control of the spray arm is proposed based on prophase research. The algorithm introduces a velocity potential field, visual field constraints and joint position limit constraints into the traditional artificial potential field method. The velocity potential field is used to ensure that the target state of the spraying arm is at the same velocity as the target crop (relative velocity) to achieve stable target tracking. The visual field constraints and joint position limit constraints are utilized to ensure the efficiency of the visual servo control and the movement of the spray arm. The algorithm can plan a feasible trajectory for the spraying arm in Cartesian space and image space, and use the speed controller to control the spraying arm movement along the trajectory for aiming and tracking. Simulation analysis shows that the algorithm can plan better motion trajectories than the servo controller based on image moments in previous studies. In addition, the experimental results show that the algorithm can effectively improve the robustness of targeting and tracking control for the spray robot.

The rotary missile stands a high overload during the launch and has to be powered up after launch, so it is necessary to achieve inflight alignment under high dynamic conditions. As a key technology of inflight alignment, the measurement method of roll angle has attracted more and more attention from researchers. The rotational speed of the rotary missile is very high, and most MIMUs cannot directly measure the roll angle. To solve this problem, this paper proposes a roll angle estimation method based on least squares method, analyzes its principle and derives the calculation procedure. Then on this basis, the roll angle estimation method based on least squares recursion is studied. The principle and calculation procedure of this method are deduced in detail. At last, the simulation experiment on MATLAB is carried out. The results show that this method is simple in calculation, high in accuracy and good in real-time performance, and has great application value.

Parametric interpolation obtains a great success in three-axis surface machining with smooth motion, high accuracy, and high machining efficiency, but does not go well in five-axis surface machining due to lack of appropriate and efficient methods of tool path generation, interpolation, and three-dimensional cutter compensation. This article proposes a triple parametric tool path interpolation method for five-axis machining with three-dimensional cutter compensation, which proposes an appropriate triple parametric tool generation method for realizing the three-dimensional cutter compensation in five-axis parametric interpolation. A triple parametric interpolation algorithm is also proposed to realizing the simultaneous interpolation of the source data, which ensures the primitivity and maintains the accuracy. The proposed three-dimensional cutter compensation can compensate the errors caused by minor changes in cutter size, thus machining accuracy can be improved. Finally, illustrated example verifies the feasibility and applicability of the proposed methods.

The measurements of geomagnetism sensors are often affected by self-factors, install-factors and environment factors, therefore they often exist error and cause measurement accuracy reduction. Focus on these problems, this paper analyse existing ground calibration algorithms and their merits and demerits respectively. Besides, a comprehensive geomagnetism sensor error model is presented. Based on this model, a least square method based ellipsoid fitting calibration and compensation algorithm is presented. The experiment results show that this novel calibration algorithm can effectively restrain and compensate geomagnetism signal measurement error.

Abstract A method was used to fabricate a fully inkjet-printed gas sensor matrix on photographic paper. An electrode matrix comprising 36 interdigital electrodes in a high-density layout that is easy to integrate has been fabricated using a combination of insulating ink and commercial silver ink. Molecular-imprinted polymer (MIP) inks were then made using a simple solution mixing method, and these inks were printed together with carbon black ink on the electrode matrix to complete production of the sensor. Finally, experimental dynamic sensing of volatile organic compounds verifies that for detection of gases corresponding to the MIP template molecules, the MIP layer offers improvements in both sensitivity and selectivity when compared with non-imprinted polymer layers. The matrix can produce a response of more than 20% to 3 ppm propenoic acid gas through adjustment of the printing times for the carbon black layer and the MIP layer.

The contact angle (CA) goniometer adaptable to a superconducting magnet was developed based on the sessile drop method. The goniometer mainly consisted of the sampling system, the supporting system, and the image acquisition system. Some improvements were taken to avoid the effects of the magnetic field (MF) on the CA measurement. As an example, the CAs of water on two substrates of silica and polymethyl methacrylate (PMMA) were measured using the goniometer. The results with and without a MF showed a good repeatability and reliability. Additionally, the MF was found to reduce the CA of water, which probably stemmed from the change of the surface tension in the MF. The CA goniometer will become an important tool which is used to study the wettability of liquids on a solid in the MF. [ABSTRACT FROM AUTHOR]

The diffusivity in the Ni-Al system with and without an alternating magnetic field (AMF) was investigated by the diffusion couple technique. It was found that the diffusion coefficient was increased with increasing AMF intensity. The enhanced diffusivity in the AMF can be ascribed to the increase in the dislocation density in the diffusion couples, which was demonstrated by the broadening of X-ray diffraction peaks. The higher dislocation density was induced by the magnetoplastic effect (MPE). The theoretical calculation based on the MPE is in agreement with the experimental values. [ABSTRACT FROM AUTHOR]

Chronic kidney disease (CKD) is a global health problem with high morbidity and mortality rate, and it induces other diseases. Since there are no obvious symptoms during the early stages of CKD, patients often fail to notice the disease. Early detection of CKD enables patients to receive timely treatment to ameliorate the progression of this disease. Machine learning models can effectively aid clinicians achieve this goal due to their fast and accurate recognition performance. In this study, we propose a machine learning methodology for diagnosing CKD. The CKD data set was obtained from the University of California Irvine (UCI) machine learning repository, which has a large number of missing values. KNN imputation was used to fill in the missing values, which selects several complete samples with the most similar measurements to process the missing data for each incomplete sample. Missing values are usually seen in real-life medical situations because patients may miss some measurements for various reasons. After effectively filling out the incomplete data set, six machine learning algorithms (logistic regression, random forest, support vector machine, k-nearest neighbor, naive Bayes classifier and feed forward neural network) were used to establish models. Among these machine learning models, random forest achieved the best performance with 99.75% diagnosis accuracy. By analyzing the misjudgments generated by the established models, we proposed an integrated model that combines logistic regression and random forest by using perceptron, which could achieve an average accuracy of 99.83% after ten times of simulation. Hence, we speculated that this methodology could be applicable to more complicated clinical data for disease diagnosis.

In previous research frameworks, researchers used an everyday dilemma to test people’s altruistic versus egoistic inclination. However, there are at least three different psychological processes that could induce altruistic over egoistic decisions, i.e., stronger altruistic sensitivity, weaker egoistic sensitivity, and stronger overall action versus inaction preference. To dissociate these different psychological processes, we developed new materials and applied the CAN algorithm from traditional moral dilemma research in two studies. In Study 1, we designed scenarios varying with a 2 (egoistic/non-egoistic) × 2 (non-altruistic/altruistic) structure. Then, we recruited 209 participants to validate the scenarios and filtered six scene frameworks with 24 scenarios in total. In Study 2, we recruited 747 participants to judge whether they would conduct behavior that is simultaneously altruistic (or non-altruistic) and egoistic (or non-egoistic) in the filtered scenarios obtained from Study 1. They also filled in the Social Isolation Scale, Distress Disclosure Scale, and some other demographic information. As we dissociated the psychological processes using the CAN algorithm, significant correlations between social isolation and distress disclosure and three parameters (i.e., altruistic tendency, egoistic tendency, and overall action/inaction preference) underlying the altruistic choice were revealed to varying degrees. Other individual differences in the psychological processes in everyday moral decision-making were further demonstrated. Our study provided materials and methodological protocols to dissociate the multiple psychological processes in everyday moral decision-making. It promotes our insights on everyday moral decisions from a differential psychological processes perspective.

It is unclear whether the state of boredom is related to morality. The present study investigated how state boredom influenced cheating behaviors on task performance. In Study 1 (N = 104), participants were induced to feel bored, and then reported whether they had finished an anagram task (two sentences in the task were unsolvable). The results found that people with higher boredom showed more cheating behaviors than those with lower boredom on task performance. In Study 2 (N = 139), participants completed the Multidimensional State Boredom Scale, and then completed the same anagram task as in Study 1, as well as a state self-efficacy scale. The results revealed that state self-efficacy mediated the effect of state boredom on cheating behaviors on task performance. In other words, a higher level of state boredom leads to a lower level of state self-efficacy, and the lower state self-efficacy then results in more cheating behaviors. The present study provides empirical evidence that state boredom has its moral function through state self-efficacy.

The CNI model generates C, N, and I parameters to measure people’s mental processes—consequence sensitivity (C), norm sensitivity (N), and generalized inaction/action preferences (I)—in moral decision making. Given the limitations of the CNI model, the CAN algorithm was developed to depict the consequence sensitivity (C), overall action versus inaction preferences (A), norm sensitivity (N), and perverse responses with the other three parameters. However, no studies have clarified whether and how the CAN algorithm can solve the limitations of the CNI model. The present study systematically uncovers the limitations of the CNI model and the solutions provided by the CAN algorithm: (a) the CNI model does not consider negative values of the parameters, but the CAN algorithm does; (b) the sequential processing assumption of the CNI model is biased, the CAN algorithm proposes a parallel calculation strategy to fix this problem; (c) the calculation of the I parameter of the CNI model is inaccurate, so the CAN algorithm proposes the A parameter to replace it; (d) the CNI model has a problem measuring perverse responses, while the CAN algorithm develops three parameters to measure these. We examined some of our points on the basis of a reanalysis of the foreign language effect (FLE) by comparing the parameters from the CAN algorithm with those from the CNI model. We found that consequence and norm sensitivity were estimated to be greater using the CNI model than with the CAN algorithm. Consequently, these overestimations significantly (consequence sensitivity) and marginally (norm sensitivity) interfered with the FLE, making the FLE more likely to return a false positive result. In addition, the CAN algorithm was able to measure the extent of perverse responses, indicating that foreign language (compared to a native language) leads to more perverse responses. The present study demonstrates that the CNI model magnifies the Type I error of conclusions and that the CAN algorithm (compared to the CNI model) provides more insights regarding moral decision making.

Recently, a multinomial process tree model was developed to measure an agent’s consequence sensitivity, norm sensitivity, and generalized inaction/action preferences when making moral decisions (CNI model). However, the CNI model presupposed that an agent considers consequences—norms—generalized inaction/action preferences sequentially, which is untenable based on recent evidence. Besides, the CNI model generates parameters at the group level based on binary categorical data. Hence, the C/N/I parameters cannot be used for correlation analyses or other conventional research designs. To solve these limitations, we developed the CAN algorithm to compute norm and consequence sensitivities and overall action/inaction preferences algebraically in a parallel manner. We re-analyzed the raw data of the original CNI model to test the methodological predictions. Our results demonstrate that: (1) the C parameter is approximately equal between the CNI model and CAN algorithm; (2) the N parameter under the CNI model approximately equals N/(1 − C) under the CAN algorithm; (3) the I parameter and A parameter are reversed around 0.5 – the larger the I parameter, the more the generalized inaction versus action preference and the larger the A parameter, the more overall action versus inaction preference; (4) tests of differences in parameters between groups with the CNI model and CAN algorithm led to almost the same statistical conclusion; (5) parameters from the CAN algorithm can be used for correlational analyses and multiple comparisons, and this is an advantage over the parameters from the CNI model. The theoretical and methodological implications of our study were also discussed.

The article presents a study on the interaction of Pen-Centric computing in designing natural user interface for multimedia information in Brown University in Providence, Rhode Island. The adaptability of Pen-Centric computing to a wide spectrum of environments distinguishes itself in conceptualizing a design. It discusses the three areas from which the study is focused including math beyond recognition, chemistry and diagramming. Additionally, the researchers suggests that pen-computing is essential in augmenting, even replacing the windows, icon, menu, pointing device (WIMP) GUI interaction with more efficient mode of interaction closer to the ideal of user-interface transparency.

An instrumented data glove with a wireless interface provides convenient and natural human-computer interaction for people with speech or hearing impairments. [ABSTRACT FROM AUTHOR]

Abstract  Three dimensional human motions recorded by motion capture and hand gestures recorded by using data gloves generate variable-length data streams. These data streams usually have dozens of attributes, and have different variations for similar motions. To segment and recognize motion streams, a classification-based approach is proposed in this paper. Classification feature vectors are extracted by utilizing singular value decompositions (SVD) of motion data. The extracted feature vectors capture the dominating geometric structures of motion data as revealed by SVD. Multi-class support vector machine (SVM) classifiers with class probability estimates are explored for classifying the feature vectors in order to segment and recognize motion streams. Experiments show that the proposed approach can find patterns in motion data streams with high accuracy. [ABSTRACT FROM AUTHOR]

Fast and accurate recognition of motion data streams from gesture sensing and motion capture devices has many applications and is the focus of this article. Based on the analysis of the geometric structures revealed by singular value decompositions (SVD) of motion data, a similarity measure is proposed for simultaneously segmenting and recognizing motion streams. A direction identification approach is explored to further differentiate motions with similar data geometric structures. Experiments show that the proposed similarity measure can segment and recognize motion streams of variable lengths with high accuracy, without knowing beforehand the number of motions in a stream. [ABSTRACT FROM AUTHOR]

Multi-attribute motion data can be generated in many applications/ devices, such as motion capture devices and animations. It can have dozens of attributes, thousands of rows, and even similar motions can have different durations and different speeds at corresponding parts. There are no row-to-row correspondences between data matrices of two motions. To be classified and recognized, multi-attribute motion data of different lengths are reduced to feature vectors by using the properties of singular value decomposition (SVD) of motion data. The reduced feature vectors of similar motions are close to each other, while reduced feature vectors are different from each other if their motions are different. By applying support vector machines (SVM) to the feature vectors, we efficiently classify and recognize real-world multi-attribute motion data. With our data set of more than 300 motions with different lengths and variations, SVM outperforms classification by related similarity measures, in terms of accuracy and CPU time. The performance of our approach shows its feasibility of real-time applications to real-world data. [ABSTRACT FROM AUTHOR]

We examined the association between education and prevalent HIV-1 infection in the Rakai district, rural Uganda based on a cross-sectional analysis of a population-based cohort. In 1990, 1397 men and 1705 women aged 13 years and older, were enrolled in 31 randomly selected communities. Strata were comprised of main road trading centres, secondary road trading villages and rural villages. Sociodemographic and behavioural data were obtained by interview and serum for HIV serostatus were obtained in the home. The analysis examines the association between sex-specific prevalent HIV infection and educational attainment, categorized as secondary, primary or none. The odds ratios (ORs) and 95% confidence intervals (95% CIs) of HIV infection were estimated, using no education as the referent group. Higher levels of education were associated with a higher HIV seroprevalence in bivariate analyses (OR 2.7 for primary and 4.1 for secondary education, relative to no education). The strength of the association was diminished but remained statistically significant after multivariate adjustment for sociodemographic and behavioural variables (adjusted OR of HIV infection 1.6 (95% CI: 1.2-2.1)) for primary education and 1.5 (95% CI: 1.0-2.2) for secondary education. Stratified multivariate analyses by place of residence indicated that the association between education and HIV prevalence was statistically significant in the rural villages, but not in the main road trading centres and intermediate trading villages. Educational attainment is a significant predictor of HIV risk in rural Uganda, in part because of risk behaviours and other characteristics among better educated individuals. Preventive interventions need to focus on better educated adults and on school-aged populations. [ABSTRACT FROM AUTHOR]

Assesses the cases of human papillomavirus (HPV) infections among women between 15-59 years old in Uganda. Intensity of the infection evaluated using the relative light unit ratio; Percentage of HPV prevalence; Significance of the study in developing population-based screening to identify women at high risk of cervical neoplasia.

The spatial updating and memory systems are employed during updating in both the immediate and retrieved environments. However, these dual systems seem to work differently, as the difference of pointing latency and absolute error between the two systems vary across environments. To verify this issue, the present study employed the bias analysis of signed errors based on the hypothesis that the transformed representation will bias toward the original one. Participants learned a spatial layout and then either stayed in the learning location or were transferred to a neighboring room directly or after being disoriented. After that, they performed spatial judgments from perspectives aligned with the learning direction, aligned with the direction they faced during the test, or a novel direction misaligned with the two above-mentioned directions. The patterns of signed error bias were consistent across environments. Responses for memory aligned perspectives were unbiased, whereas responses for sensorimotor aligned perspectives were biased away from the memory aligned perspective, and responses for misaligned perspectives were biased toward sensorimotor aligned perspectives. These findings indicate that the spatial updating system is consistently independent of the spatial memory system regardless of the environments, but the updating system becomes less accessible as the environment changes from immediate to a retrieved one.

The detection and recognition of metabolically derived aldehydes, which have been identified as important products of oxidative stress and biomarkers of cancers; are considered as an effective approach for early cancer detection as well as health status monitoring. Quartz crystal microbalance (QCM) sensor arrays based on molecularly imprinted sol-gel (MISG) materials were developed in this work for highly sensitive detection and highly selective recognition of typical aldehyde vapors including hexanal (HAL); nonanal (NAL) and bezaldehyde (BAL). The MISGs were prepared by a sol-gel procedure using two matrix precursors: tetraethyl orthosilicate (TEOS) and tetrabutoxytitanium (TBOT). Aminopropyltriethoxysilane (APT); diethylaminopropyltrimethoxysilane (EAP) and trimethoxy-phenylsilane (TMP) were added as functional monomers to adjust the imprinting effect of the matrix. Hexanoic acid (HA); nonanoic acid (NA) and benzoic acid (BA) were used as psuedotemplates in view of their analogous structure to the target molecules as well as the strong hydrogen-bonding interaction with the matrix. Totally 13 types of MISGs with different components were prepared and coated on QCM electrodes by spin coating. Their sensing characters towards the three aldehyde vapors with different concentrations were investigated qualitatively. The results demonstrated that the response of individual sensors to each target strongly depended on the matrix precursors; functional monomers and template molecules. An optimization of the 13 MISG materials was carried out based on statistical analysis such as principle component analysis (PCA); multivariate analysis of covariance (MANCOVA) and hierarchical cluster analysis (HCA). The optimized sensor array consisting of five channels showed a high discrimination ability on the aldehyde vapors; which was confirmed by quantitative comparison with a randomly selected array. It was suggested that both the molecularly imprinting (MIP) effect and the matrix effect contributed to the sensitivity and selectivity of the optimized sensor array. The developed MISGs were expected to be promising materials for the detection and recognition of volatile aldehydes contained in exhaled breath or human body odor.

The undercooling behaviors of the three metal melts with different magnetisms, i.e., paramagnetic Al and Sn and diamagnetic Zn in a steady magnetic field (SMF) up to 6 T were investigated by differential thermal analysis (DTA). It was found that regardless of magnetism, the nucleation undercooling consistently increased with increasing the SMF intensity, though the melting temperatures of the three metals were hardly varied. The invariable melting temperature results from the negligible change of Gibbs free energy under the action of the SMF. The enhanced undercooling is mainly attributed to the increase of the contact angle for heterogeneous nucleation owing to the decrease in the melt/crucible interfacial free energy induced by the SMF. In addition, the reduction of the migration rates of liquid atoms due to the Lorenz force also plays an important role on the change in undercooling in the SMF. [ABSTRACT FROM AUTHOR]

Versatile odor sensors that can discriminate among huge numbers of environmental odorants are desired in many fields, including robotics, environmental monitoring, and food production. However, odor sensors comparable to an animal’s nose have not yet been developed. An animal’s olfactory system recognizes odor clusters with specific molecular properties and uses this combinatorial information in odor discrimination. This suggests that measurement and clustering of odor molecular properties (e.g., polarity, size) using an artificial sensor is a promising approach to odor sensing. Here, adsorbents composed of composite materials with molecular recognition properties were developed for odor sensing. The selectivity of the sensor depends on the adsorbent materials, so specific polymeric materials with particular solubility parameters were chosen to adsorb odorants with various properties. The adsorption properties of the adsorbents could be modified by mixing adsorbent materials. Moreover, a novel molecularly imprinted filtering adsorbent (MIFA), composed of an adsorbent substrate covered with a molecularly imprinted polymer (MIP) layer, was developed to improve the odor molecular recognition ability. The combination of the adsorbent and MIP layer provided a higher specificity toward target molecules. The MIFA thus provides a useful technique for the design and control of adsorbents with adsorption properties specific to particular odor molecules.

Studies undertaken in Uganda to show the effects of HIV-1 and other sexually transmitted infections on pregnancy. The rural population; The cross-sectional prevalence study; The methods of the study and the results.