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Two prevalent issues with Gastrodia elata growing in a natural wild environment are the necessary symbiotic seed germination with Mycena spp. and the long growing cycle of protocorms into mature rhizomes with Armillaria mellea. This study explored the most suitable nutrients to germinate seeds and the developmental stages and propagated protocorm/juvenile rhizomes (jrhs) through a tissue culture approach. Adding humic acid, mashed potato, peptone, and (indole-3-acetic acid) IAA to the 2 g/L Hyponex 7-6-19 basal medium accelerated seed germination into protocorms with an average length of 2 mm. The nutrients best for the in vitro propagation of jrhs consisted of 1 g/L Hyponex 7-6-19 and MS salts with 1/2 NH4NO3 and KNO3, 2 mg/L BA, 2 mg/L NAA, 2 g/L tryptone, 2 g/L glutamic acid, 1 g/L chitosan, 10 g/L apple puree, 20 g/L mashed potato, 100 mL/L coconut water, and 1.5 g/L A. mellea powder, at a pH level of 6. The maximum length and number of jrhs were 17.2 ± 0.8 mm and 76.6, respectively, in two subcultures. The in vitro propagation system started from seeds of G. elata can be employed as a potential method to propagate protocorm/jrhs for the cultivation of mature rhizomes from indoor or outdoor cultures.

In order to accommodate the ever-increasing traffic demands, numerous approaches have been developed to improve spectrum utilization. Among others, the coexistence of LTE (Long-Term Evolution) and Wi-Fi, addressed by the 3GPP (3rd Generation Partnership Project) with hyper access points (HAPs) as bridges, is well recognized as a promising candidate solution. Aimed at improving the spectrum utilization of the unlicensed bands by following LTE-Unlicensed (LTE-U), this article contributes to the determination of the optimal time ratio, δ, for the time-division multiplexing of LTE and Wi-Fi over unlicensed bands. Symmetric allocation with a duty cycle of 50% cannot be an optimal option. Asymmetric allocation according to the quality of service (QoS) requirements and traffic demands should be considered. The problem is formulated as an optimization problem optimizing the total throughput. The lower and upper bounds of δ are obtained according to the QoS requirements of Wi-Fi and the admission control requirements of LTE. The detailed procedure for finding an adequate δ is developed and presented. A series of simulations are conducted to demonstrate the feasibility and effectiveness of the proposed approach. Simulation results show that the proposed approach improves the total throughput without compromising the fairness of Wi-Fi, as intended. Ten percent of improvement in throughput compared with LTE-U can be achieved.

There are many surgical operations performed daily in operation rooms worldwide. Adequate anesthesia is needed during an operation. Besides hypnosis, adequate analgesia is critical to prevent autonomic reactions. Clinical experience and vital signs are usually used to adjust the dosage of analgesics. Analgesia nociception index (ANI), which ranges from 0 to 100, is derived from heart rate variability (HRV) via electrocardiogram (ECG) signals, for pain evaluation in a non-invasive manner. It represents parasympathetic activity. In this study, we compared the performance of multilayer perceptron (MLP) and long short-term memory (LSTM) algorithms in predicting expert assessment of pain score (EAPS) based on patient′s HRV during surgery. The objective of this study was to analyze how deep learning models differed from the medical doctors′ predictions of EAPS. As the input and output features of the deep learning models, the opposites of ANI and EAPS were used. This study included 80 patients who underwent operations at National Taiwan University Hospital. Using MLP and LSTM, a holdout method was first applied to 60 training patients, 10 validation patients, and 10 testing patients. As compared to the LSTM model, which had a testing mean absolute error (MAE) of 2.633 ± 0.542, the MLP model had a testing MAE of 2.490 ± 0.522, with a more appropriate shape of its prediction curves. The model based on MLP was selected as the best. Using MLP, a seven-fold cross validation method was then applied. The first fold had the lowest testing MAE of 2.460 ± 0.634, while the overall MAE for the seven-fold cross validation method was 2.848 ± 0.308. In conclusion, HRV analysis using MLP algorithm had a good correlation with EAPS; therefore, it can play role as a continuous monitor to predict intraoperative pain levels, to assist physicians in adjusting analgesic agent dosage. Further studies may consider obtaining more input features, such as photoplethysmography (PPG) and other kinds of continuous variable, to improve the prediction performance.

The antitumor effects of Coix lacryma-jobi L. var. ma-yuen Stapf. (adlay seed) ethanolic extract have been increasingly shown. This study aimed to investigate the beneficial effects of both the fractions and subfractions of adlay seed ethanolic extract on the human breast (MCF-7) and cervical (HeLa) cancer cell lines, as well as exploring their possible mechanisms of action. The ethanolic extracts were obtained from different parts of adlay seed, including AHE (adlay hull extract), ATE (adlay testa extract), ABE (adlay bran extract) and PAE (polished adlay extract). The results of a 3-(4,5-dimethyl thiazol-2-yl)-2,5-diphenyl- tetrazolium bromide (MTT) assay showed that AHE-Ea and ATE-Ea showed significant growth inhibitory effects in a dose-dependent manner. The results also showed that the AHE-Ea-K, AHE-Ea-L, ATE-Ea-E and ATE-Ea-F subfractions inhibited cell proliferation, induced cell cycle arrest in the G0/G1 phase and decreased CDK4/Cyclin D1 protein expression. Finally, the extract activated caspase-3 activity and PARP protein expression, which induced MCF-7 and HeLa cell apoptosis. We then used liquid chromatography–mass spectrometry (LC/MS) to identify the potential active components., Quercetin showed an anticancer capacity. In conclusion, the AHE-Ea-K, AHE-Ea-L, ATE-Ea-E and ATE-Ea-F subfractions showed antitumor effects through the inhibition of MCF-7 and HeLa cell line viability, as well as inducing apoptosis and cell cycle arrest.

With conventional stethoscopes, the auscultation results may vary from one doctor to another due to a decline in his/her hearing ability with age or his/her different professional training, and the problematic cardiopulmonary sound cannot be recorded for analysis. In this paper, to resolve the above-mentioned issues, an electronic stethoscope was developed consisting of a traditional stethoscope with a condenser microphone embedded in the head to collect cardiopulmonary sounds and an AI-based classifier for cardiopulmonary sounds was proposed. Different deployments of the microphone in the stethoscope head with amplification and filter circuits were explored and analyzed using fast Fourier transform (FFT) to evaluate the effects of noise reduction. After testing, the microphone placed in the stethoscope head surrounded by cork is found to have better noise reduction. For classifying normal (healthy) and abnormal (pathological) cardiopulmonary sounds, each sample of cardiopulmonary sound is first segmented into several small frames and then a principal component analysis is performed on each small frame. The difference signal is obtained by subtracting PCA from the original signal. MFCC (Mel-frequency cepstral coefficients) and statistics are used for feature extraction based on the difference signal, and ensemble learning is used as the classifier. The final results are determined by voting based on the classification results of each small frame. After the testing, two distinct classifiers, one for heart sounds and one for lung sounds, are proposed. The best voting for heart sounds falls at 5–45% and the best voting for lung sounds falls at 5–65%. The best accuracy of 86.9%, sensitivity of 81.9%, specificity of 91.8%, and F1 score of 86.1% are obtained for heart sounds using 2 s frame segmentation with a 20% overlap, whereas the best accuracy of 73.3%, sensitivity of 66.7%, specificity of 80%, and F1 score of 71.5% are yielded for lung sounds using 5 s frame segmentation with a 50% overlap.

A new SOC (State-Of-Charge)–VOC (Voltage-of-Open-Circuit) mathematical model was proposed in this paper, which is particularly useful in parallel lithium battery modeling. When the battery strings are charged in parallel connection, the batteries can be deemed as capacitors with different capacitances, and the one with larger capacitance always obtains the higher current. According to this mathematical model, the parallel battery charging with different peak capacitances can result in different voltage slew rates on different battery strings during the constant current control. Different parallel battery strings are charged with different currents, of which the battery string under higher current can induce higher power loss and higher temperature. The conventional solution can use this model to switch the constant current charging into the constant voltage charging with the correct timing to avoid overcurrent charging. Other battery pack protection methods including current sense resistor, resettable thermal cutoff device, or resettable fuse can also use this mathematical model to improve the protection. In the experiments, three kinds of batteries including LiFePO4 battery, EV Type-1 battery, and ternary battery were examined. The experiments showed good consistency with the simulation results derived from the mathematical model.

A symmetric single-source 7-level DC-AC converter with voltage gain of 3 and self-voltage balance is presented by combining the Class-D amplifier and the diode-clamped DC-AC converter. There is only one switch is switched for any time in this circuit. As a result, the conversion efficiency can be upgraded significantly as well as the control strategy can be simplified considerably. In the paper, the operation principle and circuit design of this converter are analyzed in detail, and its feasibility and effectiveness are verified by simulation and digital control, respectively.

The flyback converter with its active cell balancing topology for charging lithium-based batteries in Electrical Vehicles (EV) have been adopted recently into the industry. Electrical Vehicle battery charging requires high current operation in continuous current mode and hence, the power loss on the Schottky diode rectifier on the secondary side determines the power conversion efficiency. The depletion mode (D-mode) GaN HEMT synchronous rectifier proposed in this paper has been used to replace the Schottky diode on the secondary side of the flyback converter in order to improve the power conversion efficiency. This synchronous rectifier regulates the forward voltage drop of an external switch to about 100 mV per ampere of current flow with no concern to threshold voltage. The first challenge of converting the D-mode GaN HEMT as a synchronous rectifier is that the normally-on device must be off when the primary side inductor of the flyback converter is initially charging the magnetic energy. That is, the rectifier must behave as the normally-off device during its initialization stage. The second challenge is that the D-mode GaN HEMT must switch off as soon as the secondary current becomes zero. The third challenge is posing a fast recovery feature to reduce the drain-source voltage rise on the primary side switch, which suffices to be the main reason as to why the D-mode GaN HEMT is used instead of MOS devices. The proposed depletion mode GaN HEMT synchronous rectifier is verified to be able to overcome all challenges and in result becomes a candidate for the synchronous rectifier.

Wearable sensors are becoming very popular recently due to their ease of use and flexibility in recording data from home [...]

The aim of this research was to evaluate the receptor tyrosine kinase inhibitor Sunitinib combined with SN-38 in polymeric micelles for antitumor efficacy in colorectal cancer. First, SN-38 and Sunitinib co-loaded micelles were developed and characterized. We studied cell viability and cellular uptake in HCT-116 cells. Then, subcutaneous HCT-116 xenograft tumors were used for ex vivo biodistribution, antitumor efficacy, and histochemical analysis studies. Results of cellular uptake and ex vivo biodistribution of SN-38/Sunitinib micelles showed the highest accumulation in tumors compared with other normal organs. In the antitumor effect studies, mice bearing HCT-116 tumors were smallest at day 28 after injection of SN-38/Sunitinib micelles, compared with other experiment groups (p < 0.01). As demonstrated by the results of inhibition on multi-receptors by Sunitinib, we confirmed that SN-38/Sunitinib co-loaded micelles to be a treatment modality that could inhibit VEGF and PDGF receptors and enhance the antitumor effect of SN-38 (p < 0.05). In summary, we consider that this micelle is a potential formulation for the combination of SN-38 and Sunitinib in the treatment of colorectal cancer.

In this paper, an effective electrocardiogram (ECG) recurrence plot (RP)-based arrhythmia classification algorithm that can be implemented in portable devices is presented. Public databases from PhysioNet were used to conduct this study including the MIT-BIH Atrial Fibrillation Database, the MIT-BIH Arrhythmia Database, the MIT-BIH Malignant Ventricular Ectopy Database, and the Creighton University Ventricular Tachyarrhythmia Database. ECG time series were segmented and converted using an RP, and two-dimensional images were used as inputs to the CNN classifiers. In this study, two-stage classification is proposed to improve the accuracy. The ResNet-18 architecture was applied to detect ventricular fibrillation (VF) and noise during the first stage, whereas normal, atrial fibrillation, premature atrial contraction, and premature ventricular contractions were detected using ResNet-50 in the second stage. The method was evaluated using 5-fold cross-validation which improved the results when compared to previous studies, achieving first and second stage average accuracies of 97.21% and 98.36%, sensitivities of 96.49% and 97.92%, positive predictive values of 95.54% and 98.20%, and F1-scores of 95.96% and 98.05%, respectively. Furthermore, a 5-fold improvement in the memory requirement was achieved when compared with a previous study, making this classifier feasible for use in resource-constricted environments such as portable devices. Even though the method is successful, first stage training requires combining four different arrhythmia types into one label (other), which generates more data for the other category than for VF and noise, thus creating a data imbalance that affects the first stage performance.

Accumulated studies indicate that zero-valent iron (ZVI) nanoparticles demonstrate endogenous cancer-selective cytotoxicity, without any external electric field, lights, or energy, while sparing healthy non-cancerous cells in vitro and in vivo. The anti-cancer activity of ZVI-based nanoparticles was anti-proportional to the oxidative status of the materials, which indicates that the elemental iron is crucial for the observed cancer selectivity. In this thematic article, distinctive endogenous anti-cancer mechanisms of ZVI-related nanomaterials at the cellular and molecular levels are reviewed, including the related gene modulating profile in vitro and in vivo. From a material science perspective, the underlying mechanisms are also analyzed. In summary, ZVI-based nanomaterials demonstrated prominent potential in precision medicine to modulate both programmed cell death of cancer cells, as well as the tumor microenvironment. We believe that this will inspire advanced anti-cancer therapy in the future.

DDoS (Distributed Denial of Service) has emerged as a serious and challenging threat to computer networks and information systems’ security and integrity. Before any remedial measures can be implemented, DDoS assaults must first be detected. DDoS attacks can be identified and characterized with satisfactory achievement employing ML (Machine Learning) and DL (Deep Learning). However, new varieties of aggression arise as the technology for DDoS attacks keep evolving. This research explores the impact of a new incarnation of DDoS attack–adversarial DDoS attack. There are established works on ML-based DDoS detection and GAN (Generative Adversarial Network) based adversarial DDoS synthesis. We confirm these findings in our experiments. Experiments in this study involve the extension and application of the GAN, a machine learning framework with symmetric form having two contending neural networks. We synthesize adversarial DDoS attacks utilizing Wasserstein Generative Adversarial Networks featuring Gradient Penalty (GP-WGAN). Experiment results indicate that the synthesized traffic can traverse the detection systems such as k-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP) and Random Forest (RF) without being identified. This observation is a sobering and pessimistic wake-up call, implying that countermeasures to adversarial DDoS attacks are urgently needed. To this problem, we propose a novel DDoS detection framework featuring GAN with Dual Discriminators (GANDD). The additional discriminator is designed to identify adversary DDoS traffic. The proposed GANDD can be an effective solution to adversarial DDoS attacks, as evidenced by the experimental results. We use adversarial DDoS traffic synthesized by GP-WGAN to train GANDD and validate it alongside three other DL technologies: DNN (Deep Neural Network), LSTM (Long Short-Term Memory) and GAN. GANDD outperformed the other DL models, demonstrating its protection with a TPR of 84.3%. A more sophisticated test was also conducted to examine GANDD’s ability to handle unseen adversarial attacks. GANDD was evaluated with adversarial traffic not generated from its training data. GANDD still proved effective with a TPR around 71.3% compared to 7.4% of LSTM.

In this paper, an isolated multi-input single-output (MISO) converter is developed and applied to a thermoelectric energy conversion system to harvest thermal energy. The thermoelectric generators have individual maximum power point tracking functions. Furthermore, such a converter has a high step-up voltage conversion ratio. In addition, the presented converter is imposed on the thermoelectric energy conversion system with the three-point weighting strategy adopted to realize the maximum power point tracking (MPPT). In this paper, the basic principles of this converter are first described and analyzed, and finally some simulated and experimental results are offered to verify the feasibility and effectiveness of such a thermal energy harvesting system.

Exosomes carry cellular proteins and contain molecules that can be potential biomarkers of diseases. This study used a Syrian golden hamster model of 7,12-dimethylbenz[a]anthracene (DMBA)-induced oral squamous cell carcinoma with radiation therapy to exclude the confounding factors that may affect outcomes in clinical studies, and re-examine the role of exosomes during tumorigenesis. We used data-dependent acquisition-based quantitative proteomics and bioinformatics analyses and found unique proteins present (desmocollin-2) or absent (Glucagon-cAMP-PKA-CREB pathway-related proteins) in the salivary exosomes of the pre-radiation DMBA-treated group (PreD). Comparing our data to other studies, salivary exosomes in the PreD group were found carrying proteins that the tumor mass does not express and lacking the proteins needed during tumorigenesis. Immunohistochemistry staining showed p53 expression but a negative apoptotic signal in the PreD tumor tissue. We thus suggest that inhibition of desmocollin-2 expression in tumor tissue may impede the activation of cell apoptosis. However, both the origin of the salivary exosomes and main role of the salivary exosome proteins should be clarified in future studies.

An Internet of Things (IoT) application typically involves implementations in both the device domain and the network domain. In this two-domain environment, it is possible that application developers implement the wrong network functions and/or connect some IoT devices that should never be linked, which result in the execution of wrong operations on network functions. To resolve these issues, we propose the VerificationTalk mechanism to prevent inappropriate IoT application deployment. VerificationTalk consists of two subsystems: the BigraphTalk subsystem which verifies IoT device configuration; and AFLtalk which validates the network functions. VerificationTalk provides mechanisms to conduct online anomaly detection by using a runtime monitor and offline by using American Fuzzy Lop (AFL). The runtime monitor is capable of intercepting potentially harmful data targeting IoT devices. When VerificationTalk detects errors, it provides feedback for debugging. VerificationTalk also assists in building secure IoT applications by identifying security loopholes in network applications. By the appropriate design of the IoTtalk execution engine, the testing capacity of AFLtalk is three times that of traditional AFL approaches.

This study evaluates cardiovascular and cerebral hemodynamics systems by only using non-invasive electrocardiography (ECG) signals. The Massachusetts General Hospital/Marquette Foundation (MGH/MF) and Cerebral Hemodynamic Autoregulatory Information System Database (CHARIS DB) from the PhysioNet database are used for cardiovascular and cerebral hemodynamics, respectively. For cardiovascular hemodynamics, the ECG is used for generating the arterial blood pressure (ABP), central venous pressure (CVP), and pulmonary arterial pressure (PAP). Meanwhile, for cerebral hemodynamics, the ECG is utilized for the intracranial pressure (ICP) generator. A deep convolutional autoencoder system is applied for this study. The cross-validation method with Pearson’s linear correlation (R), root mean squared error (RMSE), and mean absolute error (MAE) are measured for the evaluations. Initially, the ECG is used to generate the cardiovascular waveform. For the ABP system—the systolic blood pressure (SBP) and diastolic blood pressures (DBP)—the R evaluations are 0.894 ± 0.004 and 0.881 ± 0.005, respectively. The MAE evaluations for SBP and DBP are, respectively, 6.645 ± 0.353 mmHg and 3.210 ± 0.104 mmHg. Furthermore, for the PAP system—the systolic and diastolic pressures—the R evaluations are 0.864 ± 0.003 mmHg and 0.817 ± 0.006 mmHg, respectively. The MAE evaluations for systolic and diastolic pressures are, respectively, 3.847 ± 0.136 mmHg and 2.964 ± 0.181 mmHg. Meanwhile, the mean CVP evaluations are 0.916 ± 0.001, 2.220 ± 0.039 mmHg, and 1.329 ± 0.036 mmHg, respectively, for R, RMSE, and MAE. For the mean ICP evaluation in cerebral hemodynamics, the R and MAE evaluations are 0.914 ± 0.003 and 2.404 ± 0.043 mmHg, respectively. This study, as a proof of concept, concludes that the non-invasive cardiovascular and cerebral hemodynamics systems can be potentially investigated by only using the ECG signal.

This study was the first to compare the differences in trunk/shoulder kinematics and impact vibration of the upper extremity during backhand strokes in wheelchair tennis players and the able-bodied players relative to standing and sitting positions, adopting an electromagnetic system along with wearable tri-axial accelerometers upon target body segments. A total of 15 wheelchair tennis players and 15 able-bodied tennis players enrolled. Compared to players in standing positions, wheelchair players demonstrated significant larger forward trunk rotation in the pre-preparation, acceleration, and deceleration phase. Significant higher trunk angular velocity/acceleration and shoulder flexion/internal rotation angular velocity/acceleration were also found. When able-bodied players changed from standing to sitting positions, significant changes were observed in the degree of forward rotation of the trunk and shoulder external rotation. These indicated that when the functions of the lower limbs and trunk are lacking or cannot be used effectively, “biomechanical solutions” such as considerable reinforcing movements need to be made before the hitting movement. The differences between wheelchair tennis players and able-bodied players in sitting positions could represent the progress made as the wheelchair players evolve from novices to experts. Knowledge about how sport biomechanics change regarding specific disabilities can facilitate safe and inclusive participation in disability sports such as wheelchair tennis.

A non-isolated buck converter, together with resonance and zero voltage transition to achieve zero voltage switching (ZVS) and zero current switching (ZCS), is presented herein to upgrade the conversion efficiency. In this circuit, the main switch and the auxiliary switch are connected to the common ground so as to make the two switches easily driven. Furthermore, these two switches take time division multiplexing operation. In addition, the pulse width modulation (PWM) control technique is utilized so as to render the output inductor and capacitor easily designed. In this paper, the theoretical derivation is first introduced, and secondly, some experimental results are provided to demonstrate the effectiveness of the proposed topology.

A control technique combining pulse width modulation (PWM) and pulse amplitude modulation (PAM) is presented herein to reduce the output voltage ripple of the converter as little as possible. Such a converter requires a two-stage cascaded structure. The first stage is the buck-boost converter, which is used to adjust the output voltage of the second power stage, whereas the second stage is the two-phase interleaved buck converter, which is used to reduce the output voltage ripple. In theory, the two phases of the second stage operate under the condition of individual duty cycles of 50% with a phase difference of 180° between the two, and hence, the currents in the two phases are cancelled for any period of time, thereby making the output voltage of the converter almost voltage-free. Moreover, in order to improve the overall efficiency further, the proposed soft-switching technique based on an active clamp is presented and applied to these two stages to render the main and auxiliary switches turned on with zero-voltage switching (ZVS). Finally, the operating principles and control strategies of the proposed converter are described, and then, their effectiveness is verified by experimental results.

Refined cobia liver oil is a nutritional supplement (CBLO) that is rich in polyunsaturated fatty acids (PUFAs), such as DHA and EPA; however, PUFAs are prone to oxidation. In this study, the fabrication of chitosan-TPP-encapsulated CBLO nanoparticles (CS@CBLO NPs) was achieved by a two-step method, including emulsification and the ionic gelation of chitosan with sodium tripolyphosphate (TPP). The obtained nanoparticles were inspected by dynamic light scattering (DLS) and showed a positively charged surface with a z-average diameter of between 174 and 456 nm. Thermogravimetric analysis (TGA) results showed the three-stage weight loss trends contributing to the water evaporation, chitosan decomposition, and CBLO decomposition. The loading capacity (LC) and encapsulation efficiency (EE) of the CBLO loading in CS@CBLO NPs were 17.77–33.43% and 25.93–50.27%, respectively. The successful encapsulation of CBLO in CS@CBLO NPs was also confirmed by the Fourier transform infrared (FTIR) spectroscopy and X-ray diffraction (XRD) techniques. The oxidative stability of CBLO and CS@CBLO NPs was monitored by FTIR. As compared to CBLO, CS@CBLO NPs showed less oxidation with a lower generation of hydroperoxides and secondary oxidation products after four weeks of storage. CS@CBLO NPs are composed of two ingredients that are beneficial for health, chitosan and fish oil in a nano powdered fish oil form, with an excellent oxidative stability that will enhance its usage in the functional food and pharmaceutical industries.

Diabetic nephropathy (DN) is one of the most severe chronic kidney diseases in diabetes and is the main cause of end-stage renal disease (ESRD). Protocatechuic aldehyde (PCA) is a natural product with a variety of effects on pulmonary fibrosis. In this study, we examined the effects of PCA in C57BL/KS db/db male mice. Kidney morphology, renal function indicators, and Western blot, immunohistochemistry, and hematoxylin and eosin (H&E) staining data were analyzed. The results revealed that treatment with PCA could reduce diabetic-induced renal dysfunction, as indicated by the urine albumin-to-creatinine ratio (db/m: 120.1 ± 46.1μg/mg, db/db: 453.8 ± 78.7 µg/mg, db/db + 30 mg/kg PCA: 196.6 ± 52.9 µg/mg, db/db + 60 mg/kg PCA: 163.3 ± 24.6 μg/mg, p < 0.001). However, PCA did not decrease body weight, fasting plasma glucose, or food and water intake in db/db mice. H&E staining data revealed that PCA reduced glomerular size in db/db mice (db/m: 3506.3 ± 789.3 μm2, db/db: 6538.5 ± 1818.6 μm2, db/db + 30 mg/kg PCA: 4916.9 ± 1149.6 μm2, db/db + 60 mg/kg PCA: 4160.4 ± 1186.5 μm2p < 0.001). Western blot and immunohistochemistry staining indicated that PCA restored the normal levels of diabetes-induced fibrosis markers, such as transforming growth factor-beta (TGF-β) and type IV collagen. Similar results were observed for epithelial–mesenchymal transition-related markers, including fibronectin, E-cadherin, and α-smooth muscle actin (α-SMA). PCA also decreased oxidative stress and inflammation in the kidney of db/db mice. This research provides a foundation for using PCA as an alternative therapy for DN in the future.

This paper attempts to disclose a new GaN-based device, called the P-Cascode GaN HEMT, which uses only a single gate driver to control both the D-mode GaN and PMOS transistors. The merit of this synchronous buck converter is that it can reduce the circuit complexity of the synchronous buck converter, which is widely used to provide non-isolated power for low-voltage and high-current supply to system chips; therefore, the power conversion efficiency of the converter can be improved. In addition, the high side switch using a single D-mode GaN HEMT, which has no body diode, can prevent the bi-directional flow and thus reduce the power loss and cost compared to a design based on a series of two opposite MOSFETs. The experiment shows that the proposed P-Cascode GaN HEMT efficiency is above 98% when it operates at 500 kHz with 6 W output. With the input voltage at 12 V, the synchronous buck converter provides an adjustable regulated output voltage from 1.2 V to 10 V while delivering a maximum output current of 2 A.

Duchenne muscular dystrophy (DMD) results in loss of ambulation and premature death. Ultrasound provides real-time, safe, and cost-effective routine examinations. Deep learning allows the automatic generation of useful features for classification. This study utilized deep learning of ultrasound imaging for classifying patients with DMD based on their ambulatory function. A total of 85 individuals (including ambulatory and nonambulatory subjects) underwent ultrasound examinations of the gastrocnemius for deep learning of image data using LeNet, AlexNet, VGG-16, VGG-16TL, VGG-19, and VGG-19TL models (the notation TL indicates fine-tuning pretrained models). Gradient-weighted class activation mapping (Grad-CAM) was used to visualize features recognized by the models. The classification performance was evaluated using the confusion matrix and receiver operating characteristic (ROC) curve analysis. The results show that each deep learning model endows muscle ultrasound imaging with the ability to enable DMD evaluations. The Grad-CAMs indicated that boundary visibility, muscular texture clarity, and posterior shadowing are relevant sonographic features recognized by the models for evaluating ambulatory function. Of the proposed models, VGG-19 provided satisfying classification performance (the area under the ROC curve: 0.98; accuracy: 94.18%) and feature recognition in terms of physical characteristics. Deep learning of muscle ultrasound is a potential strategy for DMD characterization.

Cardiovascular diseases such as atherosclerosis and aortic valve sclerosis involve inflammatory reactions triggered by various stimuli, causing increased oxidative stress. This increased oxidative stress causes damage to the heart cells, with subsequent cell apoptosis or calcification. Currently, heart valve damage or heart valve diseases are treated by drugs or surgery. Natural antioxidant products are being investigated in related research, such as fucoxanthin (Fx), which is a marine carotenoid extracted from seaweed, with strong antioxidant, anti-inflammatory, and anti-tumor properties. This study aimed to explore the protective effect of Fx on heart valves under high oxidative stress, as well as the underlying mechanism of action. Rat heart valve interstitial cells under H2O2-induced oxidative stress were treated with Fx. Fx improved cell survival and reduced oxidative stress-induced DNA damage, which was assessed by cell viability analysis and staining with propidium iodide. Alizarin Red-S analysis indicated that Fx has a protective effect against calcification. Furthermore, Western blotting revealed that Fx abrogates oxidative stress-induced apoptosis via reducing the expression of apoptosis-related proteins as well as modulate Akt/ERK-related protein expression. Notably, in vivo experiments using 26 dogs treated with 60 mg/kg of Fx in combination with medical treatment for 0.5 to 2 years showed significant recovery in their echocardiographic parameters. Collectively, these in vitro and in vivo results highlight the potential of Fx to protect heart valve cells from high oxidative stress-induced damage.

Tongue pressure plays a critical role in the oral and pharyngeal stages of swallowing, contributing considerably to bolus formation and manipulation as well as to safe transporting of food from the mouth to the stomach. Smooth swallowing relies not only on effective coordination of respiration and pharynx motions but also on sufficient tongue pressure. Conventional methods of measuring tongue pressure involve attaching a pressure sheet to the hard palate to monitor the force exerted by the tongue tip against the hard palate. In this study, an air bulb was inserted in the anterior oral cavity to monitor the pressure exerted by the extrinsic and intrinsic muscles of the tongue. The air bulb was integrated into a noninvasive, multisensor approach to evaluate the correlation of the tongue pressure with other swallowing responses, such as respiratory nasal flow, submental muscle movement, and thyroid cartilage excursion. An autodetection program was implemented for the automatic identification of swallowing patterns and parameters from each sensor. The experimental results indicated that the proposed method is sensitive in measuring the tongue pressure, and the tongue pressure was found to have a strong positive correlation with the submental muscle movement during swallowing.

Pain is a subjective feeling; it is a sensation that every human being must have experienced all their life. Yet, its mechanism and the way to immune to it is still a question to be answered. This review presents the mechanism and correlation of pain and stress, their assessment and detection approach with medical devices and wearable sensors. Various physiological signals (i.e., heart activity, brain activity, muscle activity, electrodermal activity, respiratory, blood volume pulse, skin temperature) and behavioral signals are organized for wearables sensors detection. By reviewing the wearable sensors used in the healthcare domain, we hope to find a way for wearable healthcare-monitoring system to be applied on pain and stress detection. Since pain leads to multiple consequences or symptoms such as muscle tension and depression that are stress related, there is a chance to find a new approach for chronic pain detection using daily life sensors or devices. Then by integrating modern computing techniques, there is a chance to handle pain and stress management issue.

In this paper, an LLC light-emitting diode (LLC LED) driver based on the current-sharing capacitor is presented. In the proposed LED driver, the LLC resonant converter is used to step down the high input voltage, to provide galvanic isolation, to offer a constant current for LEDs. Moreover, the current-sharing capacitor connected to the central-tapped point of the secondary-side winding is used to balance the currents in two LED strings. By doing so, the voltage stress on this capacitor is quite low. Above all, the equivalent forward voltages of the two LED strings are generally influenced by the temperature and the LED current, and this does not affect the current-sharing performance, as will be demonstrated by experiment on the difference in number of LEDs between the two LED strings. In addition, only the current in one LED string is sensed and controlled by negative feedback control, while the current in the other LED string is determined by the current-sharing capacitor. Moreover, this makes the current control so easy. Afterwards, the basic operating principles and analyses are given, particularly for how to derive the effective resistive load from the LED string. Eventually, some experimental results are provided to validate the effectiveness of the proposed LED driver.

Object detection is an important aspect for autonomous driving vehicles (ADV), which may comprise of a machine learning model that detects a range of classes. As the deployment of ADV widens globally, the variety of objects to be detected may increase beyond the designated range of classes. Continual learning for object detection essentially ensure a robust adaptation of a model to detect additional classes on the fly. This study proposes a novel continual learning method for object detection that learns new object class(es) along with cumulative memory of classes from prior learning rounds to avoid any catastrophic forgetting. The results of PASCAL VOC 2007 have suggested that the proposed ER method obtains 4.3% of mAP drop compared against the all-classes learning, which is the lowest amongst other prior arts.

A soft switching boost converter, with a small number of components and constant frequency control, is proposed herein by using the quasi-resonance method and the zero-voltage-transition method, realizing (1) the zero-voltage switching during the switch-on transient of the main switch, (2) the zero-current switching during the switch-off transient of the main switch, (3) the zero-current switching during the switch-on transient of the auxiliary switch, and (4) the zero-current switching during the switch-off transient of the auxiliary switch. Accordingly, the corresponding efficiency can be improved. The feasibility and effectiveness of the proposed structure are validated by the field programmable gate array (FPGA).

Adolescents with intellectual disabilities display maladaptive behaviors in activities of daily living because of physical abnormalities or neurological disorders. These adolescents typically exhibit poor locomotor performance and low cognitive abilities in moving the body to perform tasks (e.g., throwing an object or catching an object) smoothly, quickly, and gracefully when compared with typically developing adolescents. Measuring movement time and distance alone does not provide a complete picture of the atypical performance. In this study, a smart ball with an inertial sensor embedded inside was proposed to measure the locomotor performance of adolescents with intellectual disabilities. Four ball games were designed for use with this smart ball: two lower limb games (dribbling along a straight line and a zigzag line) and two upper limb games (picking up a ball and throwing-and-catching). The results of 25 adolescents with intellectual disabilities (aged 18.36 ± 2.46 years) were compared with the results of 25 typically developing adolescents (aged 18.36 ± 0.49 years) in the four tests. Adolescents with intellectual disabilities exhibited considerable motor-performance differences from typically developing adolescents in terms of moving speed, hand–eye coordination, and object control in all tests.

Electroencephalography (EEG)-based brain computer interfaces (BCIs) translate motor imagery commands into the movements of an external device (e.g., a robotic arm). The automatic design of spectral and spatial filters is a challenging task, as the frequency bands of the spectral filters must be predefined by previously published studies and given that they may be affected during trials by artifacts and improper motor imagery (MI). This study aimed to eliminate the contaminated trials automatically during classifier training, and to simultaneously learn the spectral and spatial patterns without the need for predefined frequency bands. Compared with previous studies that measured the discriminative power of a frequency band based on mutual information, this study determined the difference of the class conditional probability density function between two MI classes. This information was further shared to measure the contamination level of the trial that simplified the computation. A particle-based approximation technique iteratively constructed a filter bank that extracted discriminative features, and simultaneously removed potentially contaminated trials. The particle weight was estimated by an analysis of variance F-test instead of mutual information as commonly used in previous studies. The experimental results of a publicly available dataset revealed that the proposed method outperformed the other BCI in terms of the classification accuracy. Asymmetrical spatial patterns were found on left- versus right-hand MI classifications. The learnt spectral and spatial patterns were consistent with prior neurophysiological knowledge.

Hypertension affects a huge number of people around the world. It also has a great contribution to cardiovascular- and renal-related diseases. This study investigates the ability of a deep convolutional autoencoder (DCAE) to generate continuous arterial blood pressure (ABP) by only utilizing photoplethysmography (PPG). A total of 18 patients are utilized. LeNet-5- and U-Net-based DCAEs, respectively abbreviated LDCAE and UDCAE, are compared to the MP60 IntelliVue Patient Monitor, as the gold standard. Moreover, in order to investigate the data generalization, the cross-validation (CV) method is conducted. The results show that the UDCAE provides superior results in producing the systolic blood pressure (SBP) estimation. Meanwhile, the LDCAE gives a slightly better result for the diastolic blood pressure (DBP) prediction. Finally, the genetic algorithm-based optimization deep convolutional autoencoder (GDCAE) is further administered to optimize the ensemble of the CV models. The results reveal that the GDCAE is superior to either the LDCAE or UDCAE. In conclusion, this study exhibits that systolic blood pressure (SBP) and diastolic blood pressure (DBP) can also be accurately achieved by only utilizing a single PPG signal.

Lassa virus (LASV) is a mammarenavirus (arenavirus) that causes zoonotic infection in humans that can lead to fatal hemorrhagic Lassa fever (LF) disease. Currently, there are no FDA-approved vaccines or therapeutics against LASV. Development of treatments against LF and other related arenavirus-induced hemorrhagic fevers (AHFs) requires relevant animal models that can recapitulate clinical and pathological features of AHF diseases in humans. Laboratory mice are generally resistant to LASV infection, and non-human primates, while being a good animal model for LF, are limited by their high cost. Here, we describe a small, affordable, and convenient animal model that is based on outbred Hartley guinea pigs infected with Pichinde virus (PICV), a mammarenavirus that is non-pathogenic in humans, for use as a surrogate model of human LF. We conducted a detailed analysis of tissue histopathology and immunohistochemical analysis of different organs of outbred Hartley guinea pigs infected with different PICV strains that show differential disease phenotypes and pathologies. Comparing to infection with the avirulent PICV strain (P2 or rP2), animals infected with the virulent strain (P18 or rP18) show extensive pathological changes in different organs that sustain high levels of virus replication. The similarity of tissue pathology and viral antigen distribution between the virulent PICV–guinea pig model and lethal human LASV infection supports a role of this small animal model as a surrogate model of studying human LF in order to understand its pathogenesis and for evaluating potential preventative and therapeutic options against AHFs.

Information entropy of ultrasound imaging recently receives much attention in the diagnosis of Duchenne muscular dystrophy (DMD). DMD is the most common muscular disorder; patients lose their ambulation in the later stages of the disease. Ultrasound imaging enables routine examinations and the follow-up of patients with DMD. Conventionally, the probability distribution of the received backscattered echo signals can be described using statistical models for ultrasound parametric imaging to characterize muscle tissue. Small-window entropy imaging is an efficient nonmodel-based approach to analyzing the backscattered statistical properties. This study explored the feasibility of using ultrasound small-window entropy imaging in evaluating the severity of DMD. A total of 85 participants were recruited. For each patient, ultrasound scans of the gastrocnemius were performed to acquire raw image data for B-mode and small-window entropy imaging, which were compared with clinical diagnoses of DMD by using the receiver operating characteristic curve. The results indicated that entropy imaging can visualize changes in the information uncertainty of ultrasound backscattered signals. The median with interquartile range (IQR) of the entropy value was 4.99 (IQR: 4.98–5.00) for the control group, 5.04 (IQR: 5.01–5.05) for stage 1 patients, 5.07 (IQR: 5.06–5.07) for stage 2 patients, and 5.07 (IQR: 5.06–5.07) for stage 3 patients. The diagnostic accuracies were 89.41%, 87.06%, and 72.94% for ≥stage 1, ≥stage 2, and ≥stage 3, respectively. Comparisons with previous studies revealed that the small-window entropy imaging technique exhibits higher diagnostic performance than conventional methods. Its further development is recommended for potential use in clinical evaluations and the follow-up of patients with DMD.

Our previous study showed a satisfactory reproductive outcome resulting from the patient-friendly ovarian stimulation protocol using long-acting follicle stimulation hormone (FSH) plus oral medroxyprogesterone acetate (MPA). The present retrospective study aims to compare the efficacy of the patient-friendly ovarian stimulation protocol with that of the antagonist protocol on normal and high responders aged between 24 and 39 years in a tertiary fertility center in Taiwan. To prevent premature luteinizing hormone (LH) surge, oral MPA was given to patients in group 1 (n = 57), whereas antagonist protocol was applied to group 2 (n = 53). Duration and dosage of stimulation, number of injections and visits before trigger, incidence of premature LH surge, number of oocytes retrieved, fertilization rate, cleavage rate, rate of good embryos available, incidence of ovarian hyperstimulation syndrome, cumulative clinical pregnancy rate and live birth rate per retrieval were compared between groups. We conclude that our patient-friendly ovarian stimulation protocol with MPA demonstrates satisfactory stimulation and reproductive outcomes that are comparable to those of an antagonist protocol.

An interleaved high step-down converter is presented herein, which utilized a diode-capacitor module so as to make the step-down voltage gain under the same duty cycle as well as the voltage stresses on switches and diodes relatively low as compared with the existing circuits. Also, under the same voltage gain, the proposed circuit had a relatively large duty cycle, making the elapsed time per cycle for the connection between the input and the output enlarged, and hence the controller was not interrupted by noises. This converter can be used in low-output-power high-output-current applications. In this study, the basic operating principles of the proposed converter were firstly described and analyzed, and finally, its effectiveness was demonstrated by experiment.

Dysphagia is a condition that happens when a person cannot smoothly swallow food from the mouth to the stomach. It causes malnourishment in patients, or can even cause death due to aspiration pneumonia. Recently, more and more researchers have focused their attention on the importance of swallowing and respiration coordination, and the use of non-invasive assessment systems has become a hot research trend. In this study, we aimed to integrate the timing and pattern monitoring of respiration and swallowing by using a portable and non-invasive approach which can be applied at the bedside in hospitals or institutions, or in a home environment. In this approach, we use a force sensing resistor (FSR) to detect the motions of the thyroid cartilage in the pharyngeal phase. We also use the surface electromyography (sEMG) to detect the contraction of the submental muscle in the oral phase, and a nasal cannula to detect nasal airflow for respiration monitoring during the swallowing process. All signals are received and processed for swallowing event recognition. A total of 19 volunteers participated in the testing and over 57 measurements were made. The results show that the proposed approach can effectively distinguish the swallowing function in people of different ages and genders.

Depth of anaesthesia (DoA) is an important measure for assessing the degree to which the central nervous system of a patient is depressed by a general anaesthetic agent, depending on the potency and concentration with which anaesthesia is administered during surgery. We can monitor the DoA by observing the patient’s electroencephalography (EEG) signals during the surgical procedure. Typically high frequency EEG signals indicates the patient is conscious, while low frequency signals mean the patient is in a general anaesthetic state. If the anaesthetist is able to observe the instantaneous frequency changes of the patient’s EEG signals during surgery this can help to better regulate and monitor DoA, reducing surgical and post-operative risks. This paper describes an approach towards the development of a 3D real-time visualization application which can show the instantaneous frequency and instantaneous amplitude of EEG simultaneously by using empirical mode decomposition (EMD) and the Hilbert–Huang transform (HHT). HHT uses the EMD method to decompose a signal into so-called intrinsic mode functions (IMFs). The Hilbert spectral analysis method is then used to obtain instantaneous frequency data. The HHT provides a new method of analyzing non-stationary and nonlinear time series data. We investigate this approach by analyzing EEG data collected from patients undergoing surgical procedures. The results show that the EEG differences between three distinct surgical stages computed by using sample entropy (SampEn) are consistent with the expected differences between these stages based on the bispectral index (BIS), which has been shown to be quantifiable measure of the effect of anaesthetics on the central nervous system. Also, the proposed filtering approach is more effective compared to the standard filtering method in filtering out signal noise resulting in more consistent results than those provided by the BIS. The proposed approach is therefore able to distinguish between key operational stages related to DoA, which is consistent with the clinical observations. SampEn can also be viewed as a useful index for evaluating and monitoring the DoA of a patient when used in combination with this approach.

Recently, significant developments have been achieved in the field of artificial intelligence, in particular the introduction of deep learning technology that has improved the learning and prediction accuracy to unpresented levels, especially when dealing with big data and high-resolution images. Significant developments have occurred in the area of medical signal processing, measurement techniques, and health monitoring, such as vital biological signs for biomedical systems and noise and vibration of mechanical systems, which are carried out by instruments that generate large data sets. These big data sets, ultimately driven by high population growth, would require Artificial Intelligence techniques to analyse and model. In this Special Issue, papers are presented on the latest signal processing and deep learning techniques used for health monitoring of biomedical and mechanical systems.

The emulation of human behavior for autonomous problem solving has been an interdisciplinary field of research. Generally, classical control systems are used for static environments, where external disturbances and changes in internal parameters can be fully modulated before or neglected during operation. However, classical control systems are inadequate at addressing environmental uncertainty. By contrast, autonomous systems, which were first studied in the field of control systems, can be applied in an unknown environment. This paper summarizes the state of the art autonomous systems by first discussing the definition, modeling, and system structure of autonomous systems and then providing a perspective on how autonomous systems can be integrated with advanced resources (e.g., the Internet of Things, big data, Over-the-Air, and federated learning). Finally, what comes after reaching full autonomy is briefly discussed.

Energy efficiency and renewable energy are the two main research topics for sustainable energy. In the past ten years, countries around the world have invested a lot of manpower into new energy research. However, in addition to new energy development, energy efficiency technologies need to be emphasized to promote production efficiency and reduce environmental pollution. In order to improve power production efficiency, an integrated solution regarding the issue of electric power load forecasting was proposed in this study. The solution proposed was to, in combination with persistence and search algorithms, establish a new integrated ultra-short-term electric power load forecasting method based on the adaptive-network-based fuzzy inference system (ANFIS) and back-propagation neural network (BPN), which can be applied in forecasting electric power load in Taiwan. The research methodology used in this paper was mainly to acquire and process the all-day electric power load data of Taiwan Power and execute preliminary forecasting values of the electric power load by applying ANFIS, BPN and persistence. The preliminary forecasting values of the electric power load obtained therefrom were called suboptimal solutions and finally the optimal weighted value was determined by applying a search algorithm through integrating the above three methods by weighting. In this paper, the optimal electric power load value was forecasted based on the weighted value obtained therefrom. It was proven through experimental results that the solution proposed in this paper can be used to accurately forecast electric power load, with a minimal error.

Background: A widely used method for assessing swallowing dysfunction is the videofluoroscopic swallow study (VFSS) examination. However, this method has a risk of radiation exposure. Therefore, using wearable, non-invasive and radiation-free sensors to assess swallowing function has become a research trend. This study addresses the use of a surface electromyography sensor, a nasal airflow sensor, and a force sensing resistor sensor to monitor the coordination of respiration and larynx movement which are considered the major indicators of the swallowing function. The demand for an autodetection program that identifies the swallowing patterns from multiple sensors is raised. The main goal of this study is to show that the sensor-based measurement using the proposed detection program is able to detect early-stage swallowing disorders, which specifically, are useful for the assessment of the coordination between swallowing and respiration. Methods: Three sensors were used to collect the signals from submental muscle, nasal cavity, and thyroid cartilage, respectively, during swallowing. An analytic swallowing model was proposed based on these sensors. A set of temporal parameters related to the swallowing events in this model were defined and measured by an autodetection algorithm. The verification of this algorithm was accomplished by comparing the results from the sensors with the results from the VFSS. A clinical application of the long-term smoking effect on the swallowing function was detected by the proposed sensors and the program. Results: The verification results showed that the swallowing patterns obtained from the sensors strongly correlated with the laryngeal movement monitored from the VFSS. The temporal parameters measured from these two methods had insignificant delays which were all smaller than 0.03 s. In the smoking effect application, this study showed that the differences between the swallowing function of smoking and nonsmoking participants, as well as their disorders, is revealed by the sensor-based method without the VFSS examination. Conclusions: This study showed that the sensor-based non-invasive measurement with the proposed detection algorithm is a viable method for temporal parameter measurement of the swallowing function.

Resveratrol (RSV) is a polyphenolic phytoalexin found in peanuts, grapes, and other plants. Uterine fibroids (UF) are benign growths that are enriched in extracellular matrix (ECM) proteins. In this study, we aimed to investigate the effects of RSV on UF using in vivo and in vitro approaches. In mouse xenograft models, tumors were implanted through the subcutaneous injection of Eker rat-derived uterine leiomyoma cells transfected with luciferase (ELT-3-LUC) in five-week-old female nude (Foxn1nu) mice. When the tumors reached a size of 50–100 mm3, the mice were randomly assigned to intraperitoneal treatment with RSV (10 mg·kg−1) or vehicle control (dimethyl sulfoxide). Tumor tissues were assayed using an immunohistochemistry analysis. We also used primary human leiomyoma cells as in vitro models. Cell viability was determined using the sodium bicarbonate and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. The protein expression was assayed using Western blot analysis. The messenger ribonucleic acid (mRNA) expression was assayed using quantitative reverse transcription–polymerase chain reaction (qRT–PCR). Cell apoptosis was assayed using Annexin V-fluorescein isothiocyanate (FITC) and propidium iodide (PI) and Hoechst 33342 staining. RSV significantly suppressed tumor growth in vivo and decreased the proportion of cells showing expression of proliferating cell nuclear antigen (PCNA) and α-smooth muscle actin (α-SMA). In addition, RSV decreased the protein expression of PCNA, fibronectin, and upregulated the ratio of Bax (Bcl-2-associated X) and Bcl-2 (B-cell lymphoma/leukemia 2) in vivo. Furthermore, RSV reduced leiomyoma cell viability, and decreased the mRNA levels of fibronectin and the protein expression of collagen type 1 (COL1A1) and α-SMA (ECM protein marker), as well as reducing the levels of β-catenin protein. RSV induced apoptosis and cell cycle arrest at sub-G1 phase. Our findings indicated the inhibitory effects of RSV on the ELT-3-LUC xenograft model and indicated that RSV reduced ECM-related protein expression in primary human leiomyoma cells, demonstrating its potential as an anti-fibrotic therapy for UF.

One concern to the patients is the off-line detection of pneumonia infection status after using the ventilator in the intensive care unit. Hence, machine learning methods for ventilator-associated pneumonia (VAP) rapid diagnose are proposed. A popular device, Cyranose 320 e-nose, is usually used in research on lung disease, which is a highly integrated system and sensor comprising 32 array using polymer and carbon black materials. In this study, a total of 24 subjects were involved, including 12 subjects who are infected with pneumonia, and the rest are non-infected. Three layers of back propagation artificial neural network and support vector machine (SVM) methods were applied to patients’ data to predict whether they are infected with VAP with Pseudomonas aeruginosa infection. Furthermore, in order to improve the accuracy and the generalization of the prediction models, the ensemble neural networks (ENN) method was applied. In this study, ENN and SVM prediction models were trained and tested. In order to evaluate the models’ performance, a fivefold cross-validation method was applied. The results showed that both ENN and SVM models have high recognition rates of VAP with Pseudomonas aeruginosa infection, with 0.9479 ± 0.0135 and 0.8686 ± 0.0422 accuracies, 0.9714 ± 0.0131, 0.9250 ± 0.0423 sensitivities, and 0.9288 ± 0.0306, 0.8639 ± 0.0276 positive predictive values, respectively. The ENN model showed better performance compared to SVM in the recognition of VAP with Pseudomonas aeruginosa infection. The areas under the receiver operating characteristic curve of the two models were 0.9842 ± 0.0058 and 0.9410 ± 0.0301, respectively, showing that both models are very stable and accurate classifiers. This study aims to assist the physician in providing a scientific and effective reference for performing early detection in Pseudomonas aeruginosa infection or other diseases.

EEG (Electroencephalography) signals can express the human awareness activities and consequently it can indicate the depth of anesthesia. On the other hand, Bispectral-index (BIS) is often used as an indicator to assess the depth of anesthesia. This study is aimed at using an advanced signal processing method to analyze EEG signals and compare them with existing BIS indexes from a commercial product (i.e., IntelliVue MP60 BIS module). Multivariate empirical mode decomposition (MEMD) algorithm is utilized to filter the EEG signals. A combination of two MEMD components (IMF2 + IMF3) is used to express the raw EEG. Then, sample entropy algorithm is used to calculate the complexity of the patients’ EEG signal. Furthermore, linear regression and artificial neural network (ANN) methods were used to model the sample entropy using BIS index as the gold standard. ANN can produce better target value than linear regression. The correlation coefficient is 0.790 ± 0.069 and MAE is 8.448 ± 1.887. In conclusion, the area under the receiver operating characteristic (ROC) curve (AUC) of sample entropy value using ANN and MEMD is 0.969 ± 0.028 while the AUC of sample entropy value without filter is 0.733 ± 0.123. It means the MEMD method can filter out noise of the brain waves, so that the sample entropy of EEG can be closely related to the depth of anesthesia. Therefore, the resulting index can be adopted as the reference for the physician, in order to reduce the risk of surgery.

In monitoring the depth of anesthesia (DOA), the electroencephalography (EEG) signals of patients have been utilized during surgeries to diagnose their level of consciousness. Different entropy methods were applied to analyze the EEG signal and measure its complexity, such as spectral entropy, approximate entropy (ApEn) and sample entropy (SampEn). However, as a weak physiological signal, EEG is easily subject to interference from external sources such as the electric power, electric knives and other electrophysiological signal sources, which lead to a reduction in the accuracy of DOA determination. In this study, we adopt the multivariate empirical mode decomposition (MEMD) to decompose and reconstruct the EEG recorded from clinical surgeries according to its best performance among the empirical mode decomposition (EMD), the ensemble EMD (EEMD), and the complementary EEMD (CEEMD) and the MEMD. Moreover, according to the comparison between SampEn and ApEn in measuring DOA, the SampEn is a practical and efficient method to monitor the DOA during surgeries at real time.

To assess the improvement of human body balance, a low cost and portable measuring device of center of pressure (COP), known as center of pressure and complexity monitoring system (CPCMS), has been developed for data logging and analysis. In order to prove that the system can estimate the different magnitude of different sways in comparison with the commercial Advanced Mechanical Technology Incorporation (AMTI) system, four sway tests have been developed (i.e., eyes open, eyes closed, eyes open with water pad, and eyes closed with water pad) to produce different sway displacements. Firstly, static and dynamic tests were conducted to investigate the feasibility of the system. Then, correlation tests of the CPCMS and AMTI systems have been compared with four sway tests. The results are within the acceptable range. Furthermore, multivariate empirical mode decomposition (MEMD) and enhanced multivariate multiscale entropy (MMSE) analysis methods have been used to analyze COP data reported by the CPCMS and compare it with the AMTI system. The improvements of the CPCMS are 35% to 70% (open eyes test) and 60% to 70% (eyes closed test) with and without water pad. The AMTI system has shown an improvement of 40% to 80% (open eyes test) and 65% to 75% (closed eyes test). The results indicate that the CPCMS system can achieve similar results to the commercial product so it can determine the balance.

Falls are unpredictable accidents and resulting injuries can be serious to the elderly. A preventative solution can be the use of vibration stimulus of white noise to improve the sense of balance. In this work, a pair of vibration shoes were developed and controlled by a touch-type switch which can generate mechanical vibration noise to stimulate the patient’s feet while wearing the shoes. In order to evaluate the balance stability and treatment effect of vibrating insoles in these shoes, multivariate multiscale entropy (MMSE) algorithm is applied to calculate the relative complexity index of reconstructed center of pressure (COP) signals in antero-posterior and medio-lateral directions by the multivariate empirical mode decomposition (MEMD). The results show that the balance stability of 61.5% elderly subjects is improved after wearing the developed shoes, which is more than 30.8% using multiscale entropy. In conclusion, MEMD-enhanced MMSE is able to distinguish the smaller differences between before and after the use of vibration shoes in both two directions, which is more powerful than the empirical mode decomposition (EMD)-enhanced MSE in each individual direction.

Entropy as an estimate of complexity of the electroencephalogram is an effective parameter for monitoring the depth of anesthesia (DOA) during surgery. Multiscale entropy (MSE) is useful to evaluate the complexity of signals over different time scales. However, the limitation of the length of processed signal is a problem due to observing the variation of sample entropy (SE) on different scales. In this study, the adaptive resampling procedure is employed to replace the process of coarse-graining in MSE. According to the analysis of various signals and practical EEG signals, it is feasible to calculate the SE from the adaptive resampled signals, and it has the highly similar results with the original MSE at small scales. The distribution of the MSE of EEG during the whole surgery based on adaptive resampling process is able to show the detailed variation of SE in small scales and complexity of EEG, which could help anesthesiologists evaluate the status of patients.