Enhancement of the Detection Performance of Paper-Based Analytical Devices by Nanomaterials

Paper-based analytical devices (PADs), including lateral flow assays (LFAs), dipstick assays and microfluidic PADs (μPADs), have a great impact on the healthcare realm and environmental monitoring. This is especially evident in developing countries because PADs-based point-of-care testing (POCT) enables to rapidly determine various (bio)chemical analytes in a miniaturized, cost-effective and user-friendly manner. Low sensitivity and poor specificity are the main bottlenecks associated with PADs, which limit the entry of PADs into the real-life applications. The application of nanomaterials in PADs is showing great improvement in their detection performance in terms of sensitivity, selectivity and accuracy since the nanomaterials have unique physicochemical properties. In this review, the research progress on the nanomaterial-based PADs is summarized by highlighting representative recent publications. We mainly focus on the detection principles, the sensing mechanisms of how they work and applications in disease diagnosis, environmental monitoring and food safety management. In addition, the limitations and challenges associated with the development of nanomaterial-based PADs are discussed, and further directions in this research field are proposed.

Fault Detection Method Based on Global-Local Marginal Discriminant Preserving Projection for Chemical Process

Feature extraction plays a key role in fault detection methods. Most existing methods focus on comprehensive and accurate feature extraction of normal operation data to achieve better detection performance. However, discriminative features based on historical fault data are usually ignored. Aiming at this point, a global-local marginal discriminant preserving projection (GLMDPP) method is proposed for feature extraction. Considering its comprehensive consideration of global and local features, global-local preserving projection (GLPP) is used to extract the inherent feature of the data. Then, multiple marginal fisher analysis (MMFA) is introduced to extract the discriminative feature, which can better separate normal data from fault data. On the basis of fisher framework, GLPP and MMFA are integrated to extract inherent and discriminative features of the data simultaneously. Furthermore, fault detection methods based on GLMDPP are constructed and applied to the Tennessee Eastman (TE) process. Compared with the PCA and GLPP method, the effectiveness of the proposed method in fault detection is validated with the result of TE process.

Busy and confused? High risk of missed alerts in the cockpit: an electrophysiological study

Of evolutionary importance, the ability to react to unexpected auditory stimuli remains critical today, especially in settings such as aircraft cockpits or air traffic control towers, characterized by high mental and auditory loads. Evidences show that both factors can negatively impact auditory attention and prevent appropriate reactions in hazardous situations. In the present study, sixty participants performed a simulated aviation task, varying in terms of mental load (no, low, high mental load), that was embedded with a concurrent tone detection paradigm, in which auditory load was manipulated by the number of different tones (1, 2 or 3). We measured both detection performance (miss, false alarm) and brain activity (event-related potentials) related to the target tone. Our results showed that both mental and auditory loads affected tone detection performance. Importantly, their combined effects had a massive impact on the percentage of missed target tones. While, in the no mental load condition, miss rate was very low with 1 (0.53%) and 2 tones (1.11%), it increased drastically with 3 tones (24.44%), and this effect was accentuated as mental load increased, yielding to the higher miss rate in the 3-tone paradigm under high mental load conditions (68.64%). Increased mental load, auditory load, and miss rate, were all associated with disrupted brain response to the target tone as showed by reductions of the P3b amplitude. In sum, our results highlight the importance of balancing mental and auditory loads to maintain or improve efficient reactions to alarms in complex environment.

Wearable Sensors Based on Force-Sensitive Resistors for Touch-Based Collaborative Digital Gaming

We report new classes of wearable sensors that monitor touch between fully-abled and disabled players in order to empower collaborative digital gaming between the two. Our approach relies on embroidered force-sensitive resistors (FSRs) embedded into armbands, which outperform the state-of-the-art in terms of sensitivity to low applied forces (0 to 5 N). Such low forces are of key significance to this application, given the diverse physical abilities of the players. With a focus on effective gameplay, we further explore the sensor’s touch-detection performance, study the effect of the armband fabric selection, and optimize the sensor’s placement upon the arm. Our results: (a) demonstrate a 4.4-times improvement in sensitivity to low forces compared to the most sensitive embroidered FSR reported to date, (b) confirm the sensor’s ability to empower touch-based collaborative digital gaming for individuals with diverse physical abilities, and (c) provide parametric studies for the future development of diverse sensing solutions and game applications.

Cascaded Cross-Layer Fusion Network for Pedestrian Detection

The detection method based on anchor-free not only reduces the training cost of object detection, but also avoids the imbalance problem caused by an excessive number of anchors. However, these methods only pay attention to the impact of the detection head on the detection performance, thus ignoring the impact of feature fusion on the detection performance. In this article, we take pedestrian detection as an example and propose a one-stage network Cascaded Cross-layer Fusion Network (CCFNet) based on anchor-free. It consists of Cascaded Cross-layer Fusion module (CCF) and novel detection head. Among them, CCF fully considers the distribution of high-level information and low-level information of feature maps under different stages in the network. First, the deep network is used to remove a large amount of noise in the shallow features, and finally, the high-level features are reused to obtain a more complete feature representation. Secondly, for the pedestrian detection task, a novel detection head is designed, which uses the global smooth map (GSMap) to provide global information for the center map to obtain a more accurate center map. Finally, we verified the feasibility of CCFNet on the Caltech and CityPersons datasets.

MONITORING PROCESS LOCATION AND DISPERSION SIMULTANEOUSLY USING A CONTROL REGION

Control charts are effective tools for detecting out-of-control conditions of process parameters in manufacturing and service industries. The development of distribution-free control charts is important in statistical process control when the process quality variable follows an unknown or a non-normal distribution. This research thus proposes to use a distribution-free technology to establish a new control region based on the exponentially weighted moving average median statistic and exponentially weighted moving average interquartile range statistic for simultaneously monitoring the process location and dispersion and further sets up a corresponding new control chart. We compute the out-of-control average run length to evaluate out-of-control detection performance of the proposed control region and also compare the proposed control region with some existing location and dispersion control charts. Results show that our proposed chart always exhibits superior detection performance when the shifts in process location and/or dispersion are small or moderate. The new control region is thus recommended. Keywords: control chart, distribution-free, dispersion and location, EWMA, kernel control region, kernel density estimation.

Real-time pixel-wise grasp affordance prediction based on multi-scale context information fusion

Purpose This paper aims to use fully convolutional network (FCN) to predict pixel-wise antipodal grasp affordances for unknown objects and improve the grasp detection performance through multi-scale feature fusion. Design/methodology/approach A modified FCN network is used as the backbone to extract pixel-wise features from the input image, which are further fused with multi-scale context information gathered by a three-level pyramid pooling module to make more robust predictions. Based on the proposed unify feature embedding framework, two head networks are designed to implement different grasp rotation prediction strategies (regression and classification), and their performances are evaluated and compared with a defined point metric. The regression network is further extended to predict the grasp rectangles for comparisons with previous methods and real-world robotic grasping of unknown objects. Findings The ablation study of the pyramid pooling module shows that the multi-scale information fusion significantly improves the model performance. The regression approach outperforms the classification approach based on same feature embedding framework on two data sets. The regression network achieves a state-of-the-art accuracy (up to 98.9%) and speed (4 ms per image) and high success rate (97% for household objects, 94.4% for adversarial objects and 95.3% for objects in clutter) in the unknown object grasping experiment. Originality/value A novel pixel-wise grasp affordance prediction network based on multi-scale feature fusion is proposed to improve the grasp detection performance. Two prediction approaches are formulated and compared based on the proposed framework. The proposed method achieves excellent performances on three benchmark data sets and real-world robotic grasping experiment.

Deepfake detection by human crowds, machines, and machine-informed crowds

The recent emergence of machine-manipulated media raises an important societal question: How can we know whether a video that we watch is real or fake? In two online studies with 15,016 participants, we present authentic videos and deepfakes and ask participants to identify which is which. We compare the performance of ordinary human observers with the leading computer vision deepfake detection model and find them similarly accurate, while making different kinds of mistakes. Together, participants with access to the model’s prediction are more accurate than either alone, but inaccurate model predictions often decrease participants’ accuracy. To probe the relative strengths and weaknesses of humans and machines as detectors of deepfakes, we examine human and machine performance across video-level features, and we evaluate the impact of preregistered randomized interventions on deepfake detection. We find that manipulations designed to disrupt visual processing of faces hinder human participants’ performance while mostly not affecting the model’s performance, suggesting a role for specialized cognitive capacities in explaining human deepfake detection performance.