SpeechMirror: A Multimodal Visual Analytics System for Personalized Reflection of Online Public Speaking Effectiveness.

As communications are increasingly taking place virtually, the ability to present well online is becoming an indispensable skill. Online speakers are facing unique challenges in engaging with remote audiences. However, there has been a lack of evidence-based analytical systems for people to comprehensively evaluate online speeches and further discover possibilities for improvement. This paper introduces SpeechMirror, a visual analytics system facilitating reflection on a speech based on insights from a collection of online speeches. The system estimates the impact of different speech techniques on effectiveness and applies them to a speech to give users awareness of the performance of speech techniques. A similarity recommendation approach based on speech factors or script content supports guided exploration to expand knowledge of presentation evidence and accelerate the discovery of speech delivery possibilities. SpeechMirror provides intuitive visualizations and interactions for users to understand speech factors. Among them, SpeechTwin, a novel multimodal visual summary of speech, supports rapid understanding of critical speech factors and comparison of different speech samples, and SpeechPlayer augments the speech video by integrating visualization of the speaker's body language with interaction, for focused analysis. The system utilizes visualizations suited to the distinct nature of different speech factors for user comprehension. The proposed system and visualization techniques were evaluated with domain experts and amateurs, demonstrating usability for users with low visualization literacy and its efficacy in assisting users to develop insights for potential improvement.

The mechanisms of metronidazole resistance of Helicobacter pylori: A transcriptomic and biochemical study.

Helicobacter pylori (H. pylori) is a bacterial pathogen in the stomach, causing gastritis, gastric ulcer, duodenal ulcer and even gastric cancer. The triple therapy containing one bismuth-containing compound or a proton-pump inhibitor with two antibiotics was the cornerstone of the treatment of H. pylori infections. However the drug resistance of Helicobacter pylori is more and more common, which leads to the continued decline in the radical cure rate. The purpose of this study was to investigate the mechanism of metronidazole resistance of H. pylori through transcriptomics and biochemical characterizations. In this study, a 128-time-higher metronidazole-resistant H. pylori strain compared to the sensitive strain was domesticated, and 374 significantly differential genes were identified by transcriptomic sequencing as compared to the metronidazole-sensitive strain. Through GO and KEGG enrichment analysis, antibiotic-resistance pathways were found to be mainly involved in redox, biofilm formation and ABC transportation, and the results were verified by qRT-PCR. The subsequent biochemical analysis found that the urease activity of the drug-resistant strain decreased, and whereas the capabilities of bacterial energy production, membrane production and diffusion ability increased. The work here will drop hints for the mechanisms of antibiotic-resistance of H. pylori and provide promising biomarkers for the further development of new-kind drugs to treat metronidazole-resistant H. pylori.

An Improved ResNet-1d with Channel Attention for Tool Wear Monitor in Smart Manufacturing.

Tool wear is a key factor in the machining process, which affects the tool life and quality of the machined work piece. Therefore, it is crucial to monitor and diagnose the tool condition. An improved CaAt-ResNet-1d model for multi-sensor tool wear diagnosis was proposed. The ResNet18 structure based on a one-dimensional convolutional neural network is adopted to make the basic model architecture. The one-dimensional convolutional neural network is more suitable for feature extraction of time series data. Add the channel attention mechanism of CaAt1 to the residual network block and the channel attention mechanism of CaAt5 automatically learns the features of different channels. The proposed method is validated on the PHM2010 dataset. Validation results show that CaAt-ResNet-1d can reach 89.27% accuracy, improving by about 7% compared to Gated-Transformer and 3% compared to Resnet18. The experimental results demonstrate the capacity and effectiveness of the proposed method for tool wear monitor.

Influence Mechanism of the Home Advantage on Referees' Decision-Making in Modern Football Field - A Study From Sports Neuro-Decision Science.

As professional football stadiums continue to grow in popularity worldwide, fans are able to watch the game in closer proximity, but the design of professional football stadiums to shorten the distance between fans and the playing field also exacerbates the impact of the home advantage on the referee's decision to call a penalty. Studies have confirmed the existence of the home advantage and found that experienced referees can reduce the impact of this interference, but the neural mechanisms behind this phenomenon have not been adequately investigated. In this study, we designed a soccer referee decision making task based on a home field effect scenario in a real soccer game, and used event-related potentials (ERPs) to compare the decision making and EEG differences between individuals with different experience levels when faced with foul actions under spectator noise interference. The experiments showed that individuals with different experience levels triggered a significant ERN EEG component when performing the penalty decision task under the home field effect factor, suggesting that the interference of the home field effect may lead referees to correct their previous decision-making behavior patterns in the penalty decision and reduce unfavorable calls against the home team. In contrast, referees with officiating experience elicited smaller ERN amplitudes compared to other subjects, suggesting that experience factors may inhibit this tendency to change behavioral patterns. This study suggests that in response to the increasing trend of professional football stadiums, policy makers should place more emphasis on enhancing the experience level of referees in the training of referees to ensure the fairness of the game.

MSFT-YOLO: Improved YOLOv5 Based on Transformer for Detecting Defects of Steel Surface.

With the development of artificial intelligence technology and the popularity of intelligent production projects, intelligent inspection systems have gradually become a hot topic in the industrial field. As a fundamental problem in the field of computer vision, how to achieve object detection in the industry while taking into account the accuracy and real-time detection is an important challenge in the development of intelligent detection systems. The detection of defects on steel surfaces is an important application of object detection in the industry. Correct and fast detection of surface defects can greatly improve productivity and product quality. To this end, this paper introduces the MSFT-YOLO model, which is improved based on the one-stage detector. The MSFT-YOLO model is proposed for the industrial scenario in which the image background interference is great, the defect category is easily confused, the defect scale changes a great deal, and the detection results of small defects are poor. By adding the TRANS module, which is designed based on Transformer, to the backbone and detection headers, the features can be combined with global information. The fusion of features at different scales by combining multi-scale feature fusion structures enhances the dynamic adjustment of the detector to objects at different scales. To further improve the performance of MSFT-YOLO, we also introduce plenty of effective strategies, such as data augmentation and multi-step training methods. The test results on the NEU-DET dataset show that MSPF-YOLO can achieve real-time detection, and the average detection accuracy of MSFT-YOLO is 75.2, improving about 7% compared to the baseline model (YOLOv5) and 18% compared to Faster R-CNN, which is advantageous and inspiring.

Reinforcement Learning-Based Reactive Obstacle Avoidance Method for Redundant Manipulators.

Redundant manipulators are widely used in fields such as human-robot collaboration due to their good flexibility. To ensure efficiency and safety, the manipulator is required to avoid obstacles while tracking a desired trajectory in many tasks. Conventional methods for obstacle avoidance of redundant manipulators may encounter joint singularity or exceed joint position limits while tracking the desired trajectory. By integrating deep reinforcement learning into the gradient projection method, a reactive obstacle avoidance method for redundant manipulators is proposed. We establish a general DRL framework for obstacle avoidance, and then a reinforcement learning agent is applied to learn motion in the null space of the redundant manipulator Jacobian matrix. The reward function of reinforcement learning is redesigned to handle multiple constraints automatically. Specifically, the manipulability index is introduced into the reward function, and thus the manipulator can maintain high manipulability to avoid joint singularity while executing tasks. To show the effectiveness of the proposed method, the simulation of 4 degrees of planar manipulator freedom is given. Compared with the gradient projection method, the proposed method outperforms in a success rate of obstacles avoidance, average manipulability, and time efficiency.

E-ffective: A Visual Analytic System for Exploring the Emotion and Effectiveness of Inspirational Speeches.

What makes speeches effective has long been a subject for debate, and until today there is broad controversy among public speaking experts about what factors make a speech effective as well as the roles of these factors in speeches. Moreover, there is a lack of quantitative analysis methods to help understand effective speaking strategies. In this paper, we propose E-ffective, a visual analytic system allowing speaking experts and novices to analyze both the role of speech factors and their contribution in effective speeches. From interviews with domain experts and investigating existing literature, we identified important factors to consider in inspirational speeches. We obtained the generated factors from multi-modal data that were then related to effectiveness data. Our system supports rapid understanding of critical factors in inspirational speeches, including the influence of emotions by means of novel visualization methods and interaction. Two novel visualizations include E-spiral (that shows the emotional shifts in speeches in a visually compact way) and E-script (that connects speech content with key speech delivery information). In our evaluation we studied the influence of our system on experts' domain knowledge about speech factors. We further studied the usability of the system by speaking novices and experts on assisting analysis of inspirational speech effectiveness.

Autoantibodies against p53, MMP-7, and Hsp70 as Potential Biomarkers for Detection of Nonmelanoma Skin Cancers.

Basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) are two predominant histological types of nonmelanoma skin cancer (NMSC), lacking effective early diagnostic markers. In this study, we assessed the diagnostic value of autoantibodies against p53, MMP-7, and Hsp70 in skin SCC and BCC. ELISA was performed to detect levels of autoantibodies in sera from 101 NMSC patients and 102 normal controls, who were recruited from the Cancer Hospital of Shantou University Medical College. A receiver operator characteristic curve was used to evaluate the diagnostic value. The serum levels of autoantibodies against p53, MMP-7, and Hsp70 were higher in NMSCs than those in the normal controls (all P < 0.01). The AUC of the three-autoantibody panel was 0.841 (95% CI: 0.788-0.894) with the sensitivity and specificity of 60.40% and 91.20% when differentiating NMSCs from normal controls. Furthermore, measurement of this panel could differentiate early-stage skin cancer patients from normal controls (AUC: 0.851; 95% CI: 0.793-0.908). Data from Oncomine showed that the level of p53 mRNA was elevated in BCC (P < 0.05), and the Hsp70 mRNA was upregulated in SCC (P < 0.001). This serum three-autoantibody panel might function in assisting the early diagnosis of NMSC.

Lower Tibial Tunnel Placement in Isolated Posterior Cruciate Ligament Reconstruction: Clinical Outcomes and Quantitative Radiological Analysis of the Killer Turn.

BACKGROUND: The "killer turn" effect after posterior cruciate ligament (PCL) reconstruction is a problem that can lead to graft laxity or failure. Solutions for this situation are currently lacking. PURPOSE: To evaluate the clinical outcomes of a modified procedure for PCL reconstruction and quantify the killer turn using 3-dimensional (3D) computed tomography (CT). STUDY DESIGN: Case series; Level of evidence, 4. METHODS: A total of 15 patients underwent modified PCL reconstruction with the tibial aperture below the center of the PCL footprint. Next, 2 virtual tibial tunnels with anatomic and proximal tibial apertures were created on 3D CT. All patients were assessed according to the Lysholm score, International Knee Documentation Committee (IKDC) Subjective Knee Evaluation Form, Tegner score, side-to-side difference (SSD) in tibial posterior translation using stress radiography, and 3D gait analysis. RESULTS: The modified tibial tunnel showed 2 significantly gentler turns (superior, 109.87° ± 10.12°; inferior, 151.25° ± 9.07°) compared with those reconstructed with anatomic (91.33° ± 7.28°; P < .001 for both comparisons) and proximal (99° ± 7.92°; P = .023 and P < .001, respectively) tibial apertures. The distance from the footprint to the tibial aperture was 16.49 ± 3.73 mm. All patient-reported outcome scores (mean ± SD) improved from pre- to postoperatively: Lysholm score, from 46.4 ± 18.87 to 83.47 ± 10.54 (P < .001); Tegner score, from 2.47 ± 1.85 to 6.07 ± 1.58 (P < .001); IKDC sports activities score, from 19 ± 9.90 to 33.07 ± 5.35 (P < .001); and IKDC knee symptoms score, from 17.87 ± 6.31 to 25.67 ± 3.66 (P < .001). The mean SSD improved from 9.15 ± 2.27 mm preoperatively to 4.20 ± 2.31 mm postoperatively (P < .001). The reconstructed knee showed significantly more adduction (by 1.642°), less flexion (by 1.285°), and more lateral translation (by 0.279 mm) than that of the intact knee (P < .001 for all). CONCLUSION: Lowering the tibial aperture during PCL reconstruction reduced the killer turn, and the clinical outcomes remained satisfactory. However, SSD and clinical outcomes were similar to those of previously described techniques using an anatomic tibial tunnel.