The Reliability of Transcranial Magnetic Stimulation-Derived Corticomotor Inhibition as a Brain Health Evaluation Tool in Soccer Players

Background The suitability of corticomotor inhibition and corticospinal excitability to measure brain health outcomes and recovery of sport-related head impact (concussion and subconcussion) depends on good inter-day reliability, which is evaluated in this study. Transcranial magnetic stimulation (TMS) reliability in soccer players is assessed by comparing soccer players, for whom reliability on this measure may be reduced due to exposure to head impacts, to generally active individuals not engaged in contact sport. Methods TMS-derived corticomotor inhibition and corticospinal excitability were recorded from the rectus femoris muscle during two testing sessions, spaced 1–2 weeks apart in 19 soccer players (SOC—age 22 ± 3 years) and 20 generally active (CON—age 24 ± 4 years) healthy volunteers. Inter-day reliability between the two time points was quantified by using intra-class correlation coefficients (ICC). Intra-group reliability and group differences on actual measurement values were also explored. Results Good inter-day reliability was evident for corticomotor inhibition (ICCSOC = 0.61; ICCCON = 0.70) and corticospinal excitability (ICCSOC = 0.59; ICCCON = 0.70) in both generally active individuals and soccer players routinely exposed to sport-related head impacts. Corticomotor inhibition showed lower coefficients of variation than excitability for both groups (InhibSOC = 15.2%; InhibCON = 9.7%; ExcitabSOC = 41.6%; ExcitabCON = 39.5%). No group differences between soccer players and generally active individuals were found on the corticomotor inhibition value (p > 0.05), but levels of corticospinal excitability were significantly lower in soccer players (45.1 ± 20.8 vs 85.4 ± 6.2%Mmax, p < 0.0001). Corticomotor inhibition also showed excellent inter-rater reliability (ICC = 0.87). Conclusions Corticomotor inhibition and corticospinal excitability are stable and maintain good degrees of reliability when assessed over different days in soccer players, despite their routine exposure to head impacts. However, based on intra-group reliability and group differences of the levels of excitability, we conclude that corticomotor inhibition is best suited for the evaluation of neuromuscular alterations associated with head impacts in contact sports.

A compact and miniaturized implantable antenna for ISM band in wireless cardiac pacemaker system

A tiny and compact implantable antenna for wireless cardiac pacemaker systems is designed. The antenna works in the Industrial Scientific Medical (ISM) frequency band (2.4–2.48 GHz). The size of the antenna is greatly reduced with the adoption of a high dielectric constant medium and a folded meander structure. The volume of the antenna is 4.5 mm3, and the size is only 3 mm × 3 mm × 0.5 mm. Based on the literature research, it was found that the design was the smallest among the same type of implanted antenna. The antenna is optimized and loaded with a defective slotted structure, which improves the efficiency of the overall performance of the antenna and thus the gain thereof. The antenna maintains good impedance matching in the ISM frequency band, covering the entire ISM frequency band. The actual bandwidth of the antenna is 22%, with the peak gain of − 24.9 dBi. The antenna is processed and manufactured in such a manner that the simulation keeps consistent with the actual measurement. In addition, the specific absorption rate of the antenna is also evaluated and analyzed. The result shows that this kind of antenna is the best choice to realize the wireless biological telemetry communication in the extremely compact space of the wireless cardiac pacemaker system.

Wind Power Prediction in View of Ramping Events Based on Classified Spatiotemporal Network

Wind energy has been connected to the power system on a large scale with the advantage of little pollution and large reserves. While ramping events under the influence of extreme weather will cause damage to the safe and stable operation of power system. It is significant to promote the consumption of renewable energy by improving the power prediction accuracy of ramping events. This paper presents a wind power prediction model of ramping events based on classified spatiotemporal network. Firstly, the spinning door algorithm builds parallelograms to identify ramping events from historical data. Due to the rarity of ramping events, the serious shortage of samples restricts the accuracy of the prediction model. By using generative adversarial network for training, simulated ramping data are generated to expand the database. After obtaining sufficient data, classification and type prediction of ramping events are carried out, and the type probability is calculated. Combined with the probability weight, the spatiotemporal neural network considering numerical weather prediction data is used to realize power prediction. Finally, the effectiveness of the model is verified by the actual measurement data of a wind farm in Northeast China.

Relationship between tracheal intubation and the drugs used by patients with drug overdose due to self-harm

Background Tracheal intubation may be performed in patients with drug overdose due to self-harm; however, the details of the causative drug are unknown. The purpose of this study was to clarify the relationship between drugs or its blood levels of patients with drug overdose and the need for tracheal intubation based on the actual measurement results. Methods From October 2018 to March 2020, 132 patients with drug overdose due to self-harm who were transported to the emergency department (ED) were studied. Patient drugs were measured using gas chromatography–mass spectrometry (GC-MS) and were analyzed on the basis of the GC/MS Forensic Toxicological Database. Logistic analysis was performed by combining patient information and GC-MS information. Results The Glasgow Coma Scale (GCS) and Japan Coma Scale (JCS) efficiently predicted tracheal intubation in patients with drug overdose during transport triage; GCS (cut-off value: 12, area under the curve (AUC): 0.81, 95% confidence interval (CI): 0.71–0.88, sensitivity: 0.85, specificity: 0.71, P < 0.05) and JCS (cut-off value: 3, AUC: 0.74, 95% CI: 0.60–0.84, sensitivity: 0.60, specificity: 0.84, P < 0.05). The drugs detected in all patients with drug overdose in order were benzodiazepine receptor agonists (BZs; 43.9%), anticonvulsants (38.6%), antipsychotics (25.0%), and antidepressants (9.8%). In univariate logistic analysis, antipsychotics (odds ratio (OR) 2.46, 95% CI 1.19–5.20, P < 0.05), anticonvulsants (OR 2.71, 95% CI 1.26–5.98, P < 0.05), and anticonvulsants above alert blood levels (OR 27.8, 95% CI 2.92–264.1, P < 0.05) were significantly associated with tracheal intubation in patients with drug overdose, but not BZs and antidepressants. Also, in multivariate logistic analysis, antipsychotics (OR 2.27, 95% CI 1.07–4.83, P < 0.05), anticonvulsants (OR 2.50, 95% CI 1.14–5.64, P < 0.05) and in multivariate logistic analysis of blood levels, anticonvulsants above the alert blood levels (OR 24.9, 95% CI 2.56–241.6, P < 0.05) were significantly associated with tracheal intubation in patients with drug overdose respectively. Conclusions Logistic analysis revealed that the use of anticonvulsants and antipsychotics were significantly associated with an increased OR in the tracheal intubation of patients with drug overdose due to self-harm.

Study on Self-Cleaning Time and Suspended Particle Distribution in Medical Clean Room

CFD numerical simulation of clean room in Class D medical factory was carried out and compared with the actual measurement to verify the feasibility of the simulation method. On this basis, four typical air flow organizations were simulated and compared by changing air change rate from two directions of self-cleaning time and suspended particle concentration field. According to the simulation results, in order to meet the self-cleaning time within 20 min, the best air change rate should be between 15/h and 25/h. Different air flow organizations have different self-cleaning capacity, and the value of air change rate can be relatively small in the form of single-side supply same-side down return. Different airflow organizations have different suspended particle distribution characteristics, and there are differences in the applicable scenarios, and the applicability of the top supply down return is the best.

Damage Detection and Localization under Variable Environmental Conditions Using Compressed and Reconstructed Bayesian Virtual Sensor Data

Structural health monitoring (SHM) with a dense sensor network and repeated vibration measurements produces lots of data that have to be stored. If the sensor network is redundant, data compression is possible by storing the signals of selected Bayesian virtual sensors only, from which the omitted signals can be reconstructed with higher accuracy than the actual measurement. The selection of the virtual sensors for storage is done individually for each measurement based on the reconstruction accuracy. Data compression and reconstruction for SHM is the main novelty of this paper. The stored and reconstructed signals are used for damage detection and localization in the time domain using spatial or spatiotemporal correlation. Whitening transformation is applied to the training data to take the environmental or operational influences into account. The first principal component of the residuals is used to localize damage and also to design the extreme value statistics control chart for damage detection. The proposed method was studied with a numerical model of a frame structure with a dense accelerometer or strain sensor network. Only five acceleration or three strain signals out of the total 59 signals were stored. The stored and reconstructed data outperformed the raw measurement data in damage detection and localization.

Prediction of the Collapse of Necrotic Femoral Head by CT and X-Ray Examinations before Hip Replacement Based on Intelligent Medical Big Data

It was to explore the effect of the CT and X-ray examinations before the hip replacement to predict the collapse of the necrotic femoral head under the classification of medical big data based on the decision tree algorithm of the difference grey wolf optimization (GWO) and provide a more effective examination basis for the treatment of patients with the osteonecrosis of the femoral head (ONFH). From January 2019 to January 2021, a total of 152,000 patients with ONFH and hip replacement in the tertiary hospitals were enrolled in this study. They were randomly divided into two groups, the study sample-X group (X-ray examination results) and based-CT group (CT examination results)—76,000 cases in each group. The actual measurement results of the femoral head form the gold standard to evaluate the effect of the two groups of detection methods. The measurement results of X-ray and CT before hip replacement are highly consistent with the detection results of the physical femoral head specimens, which can effectively predict the collapse of ONFH and carry out accurate staging. It is worthy of clinical promotion.

CFD Simulation of the Airflow Distribution Inside Cube-Grow

Cube-Grow was developed by MARDI to promote urban agriculture to the urban population. The product enables urban people to grow their vegetables with limited space. The initial test run of the system shows that the plant growth inside the structure was below expectation. The problem arises due to a lack of airflow or improper ventilation inside the structure. Optimum ventilation or airflow is crucial for plant growth as it enhances evapotranspiration at the leaf area to promote optimum plant growth. Therefore, this study aims to increase the airflow inside the Cube-Grow and find the best location for the air hole. Computational fluid dynamics (CFD) simulation was used in this study the analyse the effect of adding an air hole to the airflow characteristic inside the Cube-Grow. CFD also was used to select the best location to place the air hole. 3 option of air hole location was analysed and the results were compared with the existing design. The initial CFD simulation results were compared with the actual measurement data before it was used for further analysis. The result shows that adding an air hole increases overall airflow inside the Cube-Grow. Option 3 was chosen as the best location for the air hole as it produces a uniform and higher airflow inside the Cube-Grow. The study proved that CFD was able to be used to optimize the design of Cube-Grow before the actual prototype was built.

Mathematical simulation analysis of optimal detection of shot-putters’ best path

In order to consider many uncertain factors in the process of shot-put, a fuzzy optimisation model of shot-put is proposed. With the help of fuzzy anthropometric data and strength data, the model calculates the fuzzy solution set of the athlete's best throwing mode and throwing distance with a known probability distribution, which reflects the actual process of shot throwing better than the non-fuzzy optimisation model. Then, using MATLAB6 software, the program design of the model solving and the user interface of optimisation software are developed, which realises fast calculation and good user interaction function. Finally, the actual measurement data of university shot-putters are used to verify the feasibility and effectiveness of the fuzzy optimisation model.

Smartphone-Based Pedestrian Dead Reckoning for 3D Indoor Positioning

Indoor localization based on pedestrian dead reckoning (PDR) is drawing more and more attention of researchers in location-based services (LBS). The demand for indoor localization has grown rapidly using a smartphone. This paper proposes a 3D indoor positioning method based on the micro-electro-mechanical systems (MEMS) sensors of the smartphone. A quaternion-based robust adaptive cubature Kalman filter (RACKF) algorithm is proposed to estimate the heading of pedestrians based on magnetic, angular rate, and gravity (MARG) sensors. Then, the pedestrian behavior patterns are distinguished by detecting the changes of pitch angle, total accelerometer and barometer values of the smartphone in the duration of effective step frequency. According to the geometric information of the building stairs, the step length of pedestrians and the height difference of each step can be obtained when pedestrians go up and downstairs. Combined with the differential barometric altimetry method, the optimal height can be computed by the robust adaptive Kalman filter (RAKF) algorithm. Moreover, the heading and step length of each step are optimized by the Kalman filter to reduce positioning error. In addition, based on the indoor map vector information, this paper proposes a heading calculation strategy of the 16-wind rose map to improve the pedestrian positioning accuracy and reduce the accumulation error. Pedestrian plane coordinates can be solved based on the Pedestrian Dead-Reckoning (PDR). Finally, combining pedestrian plane coordinates and height, the three-dimensional positioning coordinates of indoor pedestrians are obtained. The proposed algorithm is verified by actual measurement examples. The experimental verification was carried out in a multi-story indoor environment. The results show that the Root Mean Squared Error (RMSE) of location errors is 1.04–1.65 m by using the proposed algorithm for three participants. Furthermore, the RMSE of height estimation errors is 0.17–0.27 m for three participants, which meets the demand of personal intelligent user terminal for location service. Moreover, the height parameter enables users to perceive the floor information.