Optical information hiding for different surface images.

Optical hiding often requires the selection of specific artificial optical components as carriers, which results in poor versatility of the carriers and high costs for the hiding system. To conceal secret information on different surfaces such as metal, wood, and paper, we propose an optical information hiding method. In this method, we use images of surfaces, whose grayscale histograms have the characteristic of symmetric distribution. Based on this characteristic, we first scramble the surface image, and then adjust part of the gray value of the surface image to the complementary value to embed the secret information into a scrambled surface image to generate a key image. In the extraction process, a projector is used to reproduce the scrambled surface image and the key image, which are then incoherently superimposed to extract the secret information using the human visual system. The extraction process does not require complex optical knowledge and is simple and feasible. Simulation experiments and optical experiments indicate that this method is applicable in practice and possesses good security and imperceptibility. Furthermore, we prove the reliability of this method by embedding secret information in different surface images, demonstrating the potential application of more surface images in the field of optical information hiding. Finally, we discuss the applicability of surface information images and analyze the imperceptibility of key images.

Enhancing bank marketing strategies with ensemble learning: Empirical analysis.

In order to enhance market share and competitiveness, large banks are increasingly focusing on promoting marketing strategies. However, the traditional bank marketing strategy often leads to the homogenization of customer demand, making it challenging to distinguish among various products. To address this issue, this paper presents a customer demand learning model based on financial datasets and optimizes the distribution model of bank big data channels through induction to rectify the imbalance in bank customer transaction data. By comparing the prediction models of random forest model and support vector machine (SVM), this paper analyzes the ability of the prediction model based on ensemble learning to significantly enhance the market segmentation of e-commerce banks. The empirical results reveal that the accuracy of random forest model reaches 92%, while the accuracy of SVM model reaches 87%. This indicates that the ensemble learning model has higher accuracy and forecasting ability than the single model. It enables the bank marketing system to implement targeted marketing, effectively maintain the relationship between customers and banks, and significantly improve the success probability of product marketing. Meanwhile, the marketing model based on ensemble learning has achieved a sales growth rate of 20% and improved customer satisfaction by 30%. This demonstrates that the implementation of the ensemble learning model has also significantly elevated the overall marketing level of bank e-commerce services. Therefore, this paper offers valuable academic guidance for bank marketing decision-making and holds important academic and practical significance in predicting bank customer demand and optimizing product marketing strategy.

A novel multi-scale 2D CNN with weighted focal loss for arrhythmias detection on varying-dimensional ECGs.

Objective. The ECG is a standard diagnostic tool for identifying many arrhythmias. Accurate diagnosis and early intervention for arrhythmias are of great significance to the prevention and treatment of cardiovascular disease. Our objective is to develop an algorithm that can automatically identify 30 arrhythmias by using varying-dimensional ECG signals.Approach. In this paper, we firstly proposed a novel multi-scale 2D CNN that can effectively capture pathological information from small-scale to large-scale from ECG signals to identify 30 arrhythmias from 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead ECGs. Secondly, we explored the effects of varying convolution kernels sizes and branch subnetworks on the model's performance for each arrhythmia. Thirdly, we introduced the weighted focal loss to alleviate the positive-negative class imbalance problem in the multi-label arrhythmias classification. Fourthly, we explored the utility of reduced-lead ECGs in detecting arrhythmias by comparing the performances of models on varying-dimensional ECGs.Main results. As a follow-up entry after the PhysioNet/Computing in Cardiology Challenge (2021), our proposed approach achieved the official test scores of 0.52, 0.47, 0.53, 0.51, and 0.50 for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead ECGs on the hidden test set (comparable to that of 6th, 11th, 4th, 5th, and 7th out of 39 teams in the Challenge).Significance. A multi-scale framework capable of detecting 30 arrhythmias from varying-dimensional ECGs was proposed in our work. We preliminarily verified that the multi-scale perception fields may be necessary to capture more comprehensive pathological information for arrhythmias detection. Besides, we also verified that the weighted focal loss may alleviate the positive-negative class imbalance and improve the model's generalization performance on the cross-dataset. In addition, we observed that some reduced-lead models, such as the 4-lead and 3-lead models, can even achieve performance that is almost comparable to that of the 12-lead model. The excellent performance of our proposed framework demonstrates its great potential in detecting a wide range of arrhythmias.

Polyimide-Based High-Performance Film Bulk Acoustic Resonator Humidity Sensor and Its Application in Real-Time Human Respiration Monitoring.

Respiration monitoring is vital for human health assessment. Humidity sensing is a promising way to establish a relationship between human respiration and electrical signal. This paper presents a polyimide-based film bulk acoustic resonator (PI-FBAR) humidity sensor operating in resonant frequency and reflection coefficient S11 dual-parameter with high sensitivity and stability, and it is applied in real-time human respiration monitoring for the first time. Both these two parameters can be used to sense different breathing conditions, such as normal breathing and deep breathing, and breathing with different rates such as normal breathing, slow breathing, apnea, and fast breathing. Experimental results also indicate that the proposed humidity sensor has potential applications in predicting the fitness of individual and in the medical field for detecting body fluids loss and daily water intake warning. The respiratory rates measured by our proposed PI-FBAR humidity sensor operating in frequency mode and S11 mode have Pearson correlation of up to 0.975 and 0.982 with that measured by the clinical monitor, respectively. Bland-Altman method analysis results further revealed that both S11 and frequency response are in good agreement with clinical monitor. The proposed sensor combines the advantages of non-invasiveness, high sensitivity and high stability, and it has great potential in human health monitoring.

Dual-Resonator-Based (DRB) and Multiple-Resonator-Based (MRB) MEMS Sensors: A Review.

Single-resonator-based (SRB) sensors have thrived in many sensing applications. However, they cannot meet the high-sensitivity requirement of future high-end markets such as ultra-small mass sensors and ultra-low accelerometers, and are vulnerable to environmental influences. It is fortunate that the integration of dual or multiple resonators into a sensor has become an effective way to solve such issues. Studies have shown that dual-resonator-based (DRB) and multiple-resonator-based (MRB) MEMS sensors have the ability to reject environmental influences, and their sensitivity is tens or hundreds of times that of SRB sensors. Hence, it is worth understanding the state-of-the-art technology behind DRB and MRB MEMS sensors to promote their application in future high-end markets.

Prognostic value of interleukin-6, C-reactive protein, and procalcitonin in patients with COVID-19.

BACKGROUND: The inflammatory response plays a critical role in coronavirus disease 2019 (COVID-19), and inflammatory cytokine storm increases the severity of COVID-19. OBJECTIVE: To investigate the ability of interleukin-6 (IL-6), C-reactive protein (CRP), and procalcitonin (PCT) to predict mild and severe cases of COVID-19. STUDY DESIGN: This retrospective cohort study included 140 patients diagnosed with COVID-19 from January 18, 2020, to March 12, 2020. The study population was divided into two groups according to disease severity: a mild group (MG) (n = 107) and a severe group (SG) (n = 33). Data on demographic characteristics, baseline clinical characteristics, and the levels of IL-6, CRP, and PCT on admission were collected. RESULTS: Among the 140 patients, the levels of IL-6, CRP, and PCT increased in 95 (67.9 %), 91 (65.0 %), and 8 (5.7 %) patients on admission, respectively. The proportion of patients with increased IL-6, CRP, and PCT levels was significantly higher in the SG than in the MG. Cox proportional hazard model showed that IL-6 and CRP could be used as independent factors to predict the severity of COVID-19. Furthermore, patients with IL-6 > 32.1 pg/mL or CRP > 41.8 mg/L were more likely to have severe complications. CONCLUSION: The serum levels of IL-6 and CRP can effectively assess disease severity and predict outcome in patients with COVID-19.

Seasonal responses of soil respiration to warming and nitrogen addition in a semi-arid alfalfa-pasture of the Loess Plateau, China.

Responses of soil respiration (Rs) to increasing nitrogen (N) deposition and warming will have far-reaching influences on global carbon (C) cycling. However, the seasonal (growing and non-growing seasons) difference of Rs responses to warming and N deposition has rarely been investigated. We conducted a field manipulative experiment in a semi-arid alfalfa-pasture of northwest China to evaluate the response of Rs to nitrogen addition and warming from March 2014 to March 2016. Open-top chambers were used to elevate temperature and N was enriched at a rate of 4.42g m-2yr-1 with NH4NO3. Results showed that (1) N addition increased Rs by 14% over the two-year period; and (2) warming stimulated Rs by 15% in the non-growing season, while inhibited it by 5% in the growing season, which can be explained by decreased plant coverage and soil water. The main effect of N addition did not change with time, but that of warming changed with time, with the stronger inhibition observed in the dry year. When N addition and warming were combined, an antagonistic effect was observed in the growing season, whereas a synergism was observed in the non-growing season. Overall, warming and N addition did not affect the Q10 values over the two-year period, but these treatments significantly increased the Q10 values in the growing season compared with the control treatment. In comparison, combined warming and nitrogen addition significantly reduced the Q10 values compared with the single factor treatment. These results suggest that the negative indirect effect of warming-induced water stress overrides the positive direct effect of warming on Rs. Our results also imply the necessity of considering the different Rs responses in the growing and non-growing seasons to climate change to accurately evaluate the carbon cycle in the arid and semi-arid regions.