Prediction of Mine Dust Concentration Based on Grey Markov Model

Accurate quantitative analysis and prediction of dust concentration in mines play a vital role in avoiding pneumoconiosis to a certain extent, improving industrial production efficiency, and protecting the ecological environment. The research has far-reaching significance for the prediction of dust concentration in mines in the future. Aiming at the shortcomings of the grey GM (1, 1) model in forecasting the data sequence with large random fluctuation, a grey Markov chain forecasting model is established. Firstly, considering the timeliness of monitoring data, the new dust concentration data is supplemented by using the method of cubic spline interpolation in the original data sequence. Therefore, the GM (1, 1) model is established by the method of metabolism. Then, the GM (1, 1) model is optimized by the theory of the Markov chain model. According to the relative error range generated during the prediction, the state interval is divided. Subsequently, the corresponding state probability transition matrix is constructed to obtain the grey Markov prediction model. The model was applied to the prediction of mine dust concentration and compared with the prediction results of the BP neural network model, grey prediction model, and ARIMA (1, 2, 1) model. The results showed that the prediction accuracy of the grey Markov model was significantly improved compared with other traditional prediction models. Therefore, the rationality and accuracy of this model in the prediction of mine dust concentration were verified.

Modal Parameter Identification of Structures Using Reconstructed Displacements and Stochastic Subspace Identification

A method of modal parameter identification of structures using reconstructed displacements was proposed in the present research. The proposed method was developed based on the stochastic subspace identification (SSI) approach and used reconstructed displacements of measured accelerations as inputs. These reconstructed displacements suppressed the high-frequency component of measured acceleration data. Therefore, in comparison to the acceleration-based modal analysis, the operational modal analysis obtained more reliable and stable identification parameters from displacements regardless of the model order. However, due to the difficulty of displacement measurement, different types of noise interferences occurred when an acceleration sensor was used, causing a trend term drift error in the integral displacement. A moving average low-frequency attenuation frequency-domain integral was used to reconstruct displacements, and the moving time window was used in combination with the SSI method to identify the structural modal parameters. First, measured accelerations were used to estimate displacements. Due to the interference of noise and the influence of initial conditions, the integral displacement inevitably had a drift term. The moving average method was then used in combination with a filter to effectively eliminate the random fluctuation interference in measurement data and reduce the influence of random errors. Real displacement results of a structure were obtained through multiple smoothing, filtering, and integration. Finally, using reconstructed displacements as inputs, the improved SSI method was employed to identify the modal parameters of the structure.

Vaccination as an Alternative to Non-Drug Interventions to Prevent Local Resurgence of COVID-19

Background While a COVID-19 vaccine protects people from serious illness and death, it remains concern when and how to relax from the high cost strict non-pharmaceutical interventions (NPIs). Methods We developed a stochastic calculus model to identify the level of vaccine coverage that would allow safe relaxation of NPIs, and the vaccination strategies that can best achieve this level of coverage. We applied Monto Carlo simulations more than 10,000 times to remove random fluctuation effects and obtain fitted/predicted epidemic curve based on various parameters with 95% confidence interval (95% CI) at each time point. Results We found that a vaccination coverage of 50.42% was needed for the safe relaxation of NPIs, if the vaccine effectiveness was 79.34%. However, with the increasing of variants transmissibility and the decline of vaccine effectiveness for variants, the threshold for lifting NPIs would be higher. We estimated that more than 8 months were needed to achieve the vaccine coverage threshold in the combination of accelerated vaccination strategy and key groups firstly strategy. Conclusion If there are sufficient doses of vaccine then an accelerated vaccination strategy should be used, and if vaccine supply is insufficient then high-risk groups should be targeted for vaccination first. Sensitivity analyses results shown that the higher the transmission rate of the virus and the lower annual vaccine supply, the more difficult the epidemic could be under control. In conclusion, as vaccine coverage improves, the NPIs can be gradually relaxed. Until that threshold is reached, however, strict NPIs are still needed to contain the epidemic. The more transmissible SARS-CoV-2 variant lead to higher resurgence probability, which indicates the importance of accelerated vaccination and achieving the vaccine coverage earlier. Trial registration We did not involve clinical trial.

A Corner Detection Algorithm Based on Regional Center of Mass in Imaging through Water Surface

Structural light active imaging can obtain more information about the target scene, which is widely used in image registration,3D reconstruction of objects and motion detection. Due to the random fluctuation of water surface and complex underwater environment, the current corner detection algorithm has the problems of false detection and uncertainty. This paper proposes a corner detection algorithm based on the region centroid extraction. Experimental results show that, compared with the traditional detection algorithms, the proposed algorithm can extract the feature point information of the image in real time, which is of great significance to the subsequent image restoration.

Computer Image Recognition and Recovery Method for Distorted Underwater Images by Structural Light

When imaging through water surface, the random fluctuation of sea surface will cause the distortion of the target scene image, so the distorted image needs to be corrected and reconstructed. At present, distortion compensation mainly adopts iterative registration strategy based on image sequences which is difficult to satisfy the real-time observation. This paper presents a correction method based on active imaging of structured light for underwater image. Experimental results show that compared with the traditional iterative algorithm, the proposed algorithm cannot only improve the restoration accuracy, but also greatly shorten the processing time. Experimental test results demonstrate that the proposed algorithm has good recovery results.

Response Statistics of a Shape Memory Alloy Oscillator with Random Excitation

This paper aimed to explore analytically the influences of random excitation on a shape memory alloy (SMA) oscillator. Firstly, on the basis of the deterministic SMA model under a harmonic excitation, we introduce a stochastic SMA model with a narrow-band random excitation. Subsequently, a theoretical analysis for the proposed SMA model was achieved through a multiple-scale method coupled with a perturbation technique. All of the obtained approximate analytical solutions were verified by numerical simulation results, and good agreements were observed. Then, effects of the random excitation and the temperature value on the system responses were investigated in detail. Finally, we found that stochastic switch and bifurcation can be induced by the random fluctuation, which were further illustrated through time history and steady-state probability density function. These results indicate that the random excitation has a significant impact on dynamics of the SMA model. This research provides a certain theoretical basis for the design and vibration control of the SMA oscillator in practical application.

Attention-Based STL-BiLSTM Network to Forecast Tourist Arrival

Tourism makes a significant contribution to the economy of almost every country, so accurate demand forecasting can help in better planning for the government and a range of stakeholders involved in the tourism industry and can aid economic sustainability. Machine learning models, and in particular, deep neural networks, can perform better than traditional forecasting models which depend mainly on past observations (e.g., past data) to forecast future tourist arrivals. However, search intensities indices (SII) indicators have recently been included as a forecasting model, which significantly enhances forecasting accuracy. In this study, we propose a bidirectional long short-term memory (BiLSTM) neural network to forecast the arrival of tourists along with SII indicators. The proposed BiLSTM network can remember information from left to right and right to left, which further adds more context for forecasting in memory as compared to a simple long short- term memory (LSTM) network that can remember information only from left to right. A seasonal and trend decomposition using the Loess (STL) approach is utilized to decompose time series tourist arrival data suggested by previous studies. The resultant approach, called STL-BiLSTM, decomposes time series into trend, seasonality, and residual. The trend provides the general direction of the overall data. Seasonality is a regular and predictable pattern which re-occurs at fixed time intervals, and residual is a random fluctuation that is something which cannot be forecast. The proposed BiLSTM network achieves better accuracy than the other methods considered under the current study.

COVID-19 in the Clinic: Human Testing of an Aerosol Containment Mask for Endoscopic Clinic Procedures

Objective To create an aerosol containment mask (ACM) for common otolaryngologic endoscopic procedures that also provides nanoparticle-level protection to patients. Study Design Prospective feasibility study . Setting In-person testing with a novel ACM. Methods The mask was designed in Solidworks and 3D printed. Measurements were made on 10 healthy volunteers who wore the ACM while reading the Rainbow Passage repeatedly and performing a forced cough or sneeze at 5-second intervals over 1 minute with an endoscope in place. Results There was a large variation in the number of aerosol particles generated among the volunteers. Only the sneeze task showed a significant increase compared with normal breathing in the 0.3-µm particle size when compared with a 1-tailed t test ( P = .013). Both the 0.5-µm and 2.5-µm particle sizes showed significant increases for all tasks, while the 2 largest particle sizes, 5 and 10 µm, showed no significant increase (both P < .01). With the suction off, 3 of 30 events (2 sneeze events and 1 cough event) had increases in particle counts, both inside and outside the mask. With the suction on, 2 of 30 events had an increase in particle counts outside the mask without a corresponding increase in particle counts inside the mask. Therefore, these fluctuations in particle counts were determined to be due to random fluctuation in room particle levels. Conclusion ACM will accommodate rigid and flexible endoscopes plus instruments and may prevent the leakage of patient-generated aerosols, thus avoiding contamination of the room and protecting health care workers from airborne contagions. Level of evidence 2

Trends of follow-up clinic visits and admissions three-months before and during COVID-19 pandemic at Tikur Anbessa specialized hospital, Addis Ababa, Ethiopia: an interrupted time series analysis

Background Following the first report of the COVID-19 case in Ethiopia on March 13, 2020, the country promptly adopted a lockdown policy to contain the virus’s spread. Responding to the healthcare burden imposed by the COVID-19 pandemic had to be coupled with ensuring essential health care services. This study assessed the impact of COVID-19 on the trends in hospital visits and admissions at Tikur Anbessa Specialized Hospital by comparing the rate of follow-up clinic visits and admissions for the 3 months before and after the first report of the COVID-19 case. Methods A retrospective, time-series study examined the trend in follow-up visits and admissions between December 11, 2019, to June 7, 2020, with the 1st case of the COVID-19 report in Ethiopia (March 13, 2020) as a reference time. To control seasonal effects and random fluctuation, we have compared health care utilization to its equivalent period in 2018/19. A data extraction tool was used to collect secondary data from each unit’s electronic medical recordings and logbooks. Results A total of 7717 visits from eight follow-up clinics and 3310 admissions were collected 3 months before the onset of COVID-19. During the following 3 months after the onset of the pandemic, 4597 visits and 2383 admissions were collected. Overall, a 40.4% decrease in follow-up visits and a 28% decline in admissions were observed during the COVID-19 pandemic. A drop in the daily follow-up visits was observed for both genders. The number of visits in all follow-up clinics in 2019/2020 decreased compared to the same months in 2018/19 (p < 0.05). Follow-up visits were substantially lower for renal patients (− 68%), patients with neurologic problems (− 53.9%), antiretroviral treatment clinics (− 52.3%), cardiac patients (− 51.4%). Although pediatric emergency admission was significantly lower (− 54.1%) from the baseline (p = 0.04), admissions from the general pediatric and adult wards did not show a significant difference. Conclusions A decline in follow-up clinic visits and emergency admissions was observed during the first months of the COVID-19 pandemic. This will increase the possibility of avoidable morbidity and mortality due to non-COVID-19-related illnesses. Further studies are needed to explore the reasons for the decline and track the pandemic’s long-term effects among non-COVID-19 patients.

Construction of Accurate Crack Identification on Concrete Structure using Hybrid Deep Learning Approach

In general, several conservative techniques are available for detecting cracks in concrete bridges but they have significant limitations, including low accuracy and efficiency. Due to the expansion of the neural network method, the performance of digital image processing based crack identification has recently diminished. Many single classifier approaches are used to detect the cracks with high accuracy. The classifiers are not concentrating on random fluctuation in the training dataset and also it reflects in the final output as an over-fitting phenomenon. Though this model contains many parameters to justify the training data, it fails in the residual variation. These residual variations are frequent in UAV recorded photos as well as many camera images. To reduce this challenge, a noise reduction technique is utilized along with an SVM classifier to reduce classification error. The proposed technique is more resourceful by performing classification via SVM approach, and further the feature extraction and network training has been implemented by using the CNN method. The captured digital images are processed by incorporating the bending test through reinforced concrete beams. Moreover, the proposed method is determining the widths of the crack by employing binary conversion in the captured images. The proposed model outperforms conservative techniques, single type classifiers, and image segmentation type process methods in terms of accuracy. The obtained results have proved that, the proposed hybrid method is more accurate and suitable for crack detection in concrete bridges especially in the unmanned environment.