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Pendubot has been widely applied as a benchmark platform for control research and education. In this paper, a novel adaptive fuzzy hierarchical sliding mode controller (AFHSMC) is proposed for the pendubot under actuator faults and uncertainties. The proposed controller is designed by combining hierarchical sliding mode control (HSMC), fuzzy logic control (FLC), and balancing composite motion optimization. The proposed controller preserves many advantages such as having a straightforward structure, simple implementation, chattering reduction, and high precision and robustness. The stability of the proposed controller is ensured by using the Lyapunov approach. To verify the control performance, various numerical simulations and experiments are conducted on a pendubot under conditions that involve actuator faults and uncertainties. Compared to the conventional HSMC and FHSMC controllers, the proposed AFHSMC improves by 0.43% and 0.38% for tracking precision of the first link's angle estimate, 3.26% and 0.08% for the second link's angle estimate when influenced by uncertainties, as well as 65.23% and 12.24% for the first link, 83.95% and 16.15% for the second link when influenced by faults.

Purpose: This study aims to evaluate axonal injury in supratentorial acute stroke patients and predict motor function recovery. Methods: A cross-sectional descriptive study was performed on 28 patients with supratentorial acute stroke. All patients underwent brain magnetic resonance imaging (MRI) at Bach Mai Hospital from September 2021 to August 2022. Diffuse tensor imaging (DTI) was conducted using a 3-Tesla MRI machine to evaluate the association between the corticospinal tract and infarct area. Therefore, axonal injury and motor function recovery levels could be predicted. Results: Almost patients had no change in the signal of the axons, which did not pass through the infarct lesion (28.6%), and the signals strongly decreased in patients whose axons completely stayed inside the infarct lesion (32.1%). The fractional anisotropy (FA) and apparent diffusion coefficient (ADC) indices of the axons on the infarcted side were lower than those of axons on the contralateral side. The patient group, in which the axons did not pass through the infarct lesion or had no change in axonal signals, had a higher rate of better motor function recovery after 3 months than the other groups (39.3% and 25 %, respectively), whereas the axon group, in which the axons stayed completely inside the infarct lesion or had a remarkably decreased signal, had a very poor rate of recovery (32.1% and 39.3 %, respectively). The FA index of the axons on the infarct side in the poor recovery group was lower than that in the good recovery group. The ADC index did not differ between the groups. Conclusion: MRI finding with axonal signal, location, and infarct side could significantly predict motor recovery after 3 months in acute stroke patients.

Automatic analysis of medical images and endoscopic images, in particular, is an attractive research topic in recent years. To achieve this goal, many tasks must be conducted for example lesion detection, segmentation, and classification. However, existing methods for such problems still face challenges due to the spreading characteristic of the lesions as well as some artifacts caused by motion, specularities, low contrast, bubbles, debris, bodily fluid, and blood. As a consequence, single segmentation or detection could not deal with those issues. Besides, although deep learning has achieved impressive results in many tasks, lacking a large annotated dataset could lead to overfitting problems. In this paper, we tackle these issues by taking particular characteristics of lesions of interest and advantages of deep segmentation models into account. We propose a dual-path framework (namely, DCS-UNet) that combines both segmentation and classification. We first segment lesions from the image using U-Net architecture with different encoder backbones (ResNet-50, VGG-16, DenseNet-201). The segmented regions will be then refined in the second path where we classify every patch in the whole image or the Inner or Outer regions extended from the contours given by the segmentation results. For the refinement scheme, we utilize the color-dependent and texture features as doctors’ advice and deploy the support vector machine (SVM) technique to classify a patch into disease region or not. Extensive experiments were conducted on three datasets of gastroesophageal reflux disease (GERD) endoscopic images as GradeM, GradeA, GradeB, which are defined as modified Los Angeles classification. The experimental results show the improvements of the proposed method compared to the single U-Net or a segmentation using the hand-designed features-based scheme. The proposed method improves mDice and mIoU by 0.5% and 0.36% on GradeA dataset, which is the most challenging dataset with ambiguous separations of GERD vs. normal regions, and by 0.82% and 0.81% on GradeM dataset, which is clearer to segment GERD regions. This proposed framework shows the potential to develop a diagnosis assistance system to help endoscopists reduce burden in examining GERD in the future.

Objective To determine the value of endometrial thickness (ET) and Doppler indices of uterine artery (UtA) as sonographic markers in predicting endometrial cancer (EC) among postmenopausal bleeding (PMB) women in low-resource settings as Vietnam. Methods This cross-sectional study was conducted at the Hue University Hospital and Hue Central Hospital between June 2016 and June 2019. The study enrolled all women who complained of PMB and were followed by transvaginal Doppler ultrasound. Their definitive histopathological examination was the gold standard for comparison. Results The UtA Doppler indices, including resistance index (RI), pulsatility index (PI), and peak systolic velocity (PSV), were significantly lower in the malignant group than in the benign group. The threshold values of the UtA, RI ≤0.73 and PI ≤1.42, were found with an area under receiver operating characteristic curve (AUC) of 0.85–0.88, and the sensitivity and specificity were 91.3% and 83.3%, respectively. Unlike PSV, the diagnostic value was the lowest, with an AUC of 0.72. ET was a good predictor for the diagnosis of EC, with an AUC of 0.89. In women with PMB, when using the cutoff value of EC more than 12.5 mm, the sensitivity and specificity were 93.8% and 77.8%, respectively. In addition, the higher the stage of EC, the lower the RI and PI and the greater the EC. Conclusion ET, and RI, PI, and PSV of the UtA could help in differentiating malignant from benign endometrial changes. Pulsed ultrasonic Doppler velocimetry seems to play a role in predicting the higher stages of EC. Further studies are needed to confirm these findings.

Abstract The influence of various drying characteristics in the experiment was explored in this study. The drying time and moisture content were used to evaluate the experimental outcomes. The drying of bitter gourd slices using a halogen dryer was done at varied thicknesses (3, 5, and 7 mm) and temperatures (60 °C, 65 °C and 70 °C). The results revealed that the drying time and equilibrium moisture content are considerably affected by the material drying thickness and drying temperature. Furthermore, the Generalized Regression Neural Network (GRNN) model is employed in this study to train and predict the moisture content of bitter gourd as an output parameter. The temperature, bitter gourd thickness, and drying time were considered as input parameters for the GRNN model. Three statistic measures as the R-square, the Root mean square error (RMSE) and the Mean relative percent error (P) were used to validate the accuracy of the trained GRNN model. In training with nine experimental condition datasets, the average score values of R-square, RMSE and P were obtained at 0.995197, 1.498966 and 0.091617, respectively. The test of trained GRNN has been conducted with good agreement between experimental data points and predicted points. The result revealed that GRNN was effective in predicting the moisture content of bitter gourd in a halogen dryer.

Background: Intracavitary uterine pathologies (IUPs) may be resulting in vaginal bleeding in perimenopausal and postmenopausal women. Especially, malignant disease needed to be investigated due to its adverse impact on the quality of mid-life women as well as the burden of health-care costs in low- and middle-income countries such as Vietnam. Objective: Thereby, through this study, we aimed to assess the major clinical features of IUPs in women with perimenopausal and postmenopausal bleeding women. Materials and Methods: This was a cross-sectional observational study at Hue University Hospital and Hue Central Hospital from June 2016 to June 2019. The study enrolled 150 women above 40 years old involving with intrauterine bleeding. Results: The mean age of the study population was 51.51 ± 7.65. The most common symptom in perimenopausal women was menorrhagia, up to 62.2% of cases. In addition to intrauterine bleeding, there were 54.7% of cases had at least one other functional symptom, they were pale skin (35.9%), and lower abdominal pain (31.3). Endometrial hyperplasia (EH) was the most common pathology in both groups with perimenopausal and postmenopausal bleeding (PMB), respectively, 66.7% and 51.7%. In PMB group, endometrial cancer (EC) occupied approximately 38.3% following EH. Our study revealed age, menstrual characteristics, and diabetes in relation to malignant disease. Other factors seemed to be less associated with EC. Conclusions: Menorrhagia was the most common type of abnormal uterine bleeding in perimenopausal women. Besides vaginal bleeding, others symptoms such as pale skin, pelvic pain, and fatigue were also common. Length day and blood loss before hospitalization in perimenopausal women were greater significantly than that in postmenopausal women. Age, menstrual characteristics, and diabetes increased the risk of EC in women with PMB.

Objectives: To determine the role of B-mode ultrasonography combined with Doppler ultrasonography in diagnosing uterine intracavitary pathology in perimenopausal and postmenopausal women with abnormal uterine bleeding (AUB). Patients and Methods: This prospective observational study included 150 women aged >40 years with AUB hospitalized at Hue University Hospital and Hue Central Hospital between 6/2016 and 6/2019. All participants were investigated by B-mode transvaginal ultrasound and Doppler transvaginal ultrasound, and the result of sonography was compared to the histopathological endpoint. Results: The morphological features, structure, margin, border line of the endometrial-mass lesion, intracavitary uterine fluid, and Doppler signal clearly differed between benign and malignant intracavitary pathologies (P < 0.0001). However, echogenicity had a limited value in distinguishing between uterine intracavitary pathologies (P = 0.1). The sensitivity and specificity of the pedicle sign in diagnosing endometrial polyps were 50.0% and 97.6%, respectively; for the circular pattern in subendometrial fibroids were 46.2% and 100.0%, respectively; for the multiple vessel pattern in endometrial cancer were 64.0% and 96.0%, respectively, and for the scattered-vessel pattern in endometrial hyperplasia were 43.96% and 56.43%, respectively. Conclusions: Ultrasound B-mode combined with Doppler as a noninvasive tool was significantly valuable in the diagnostic procedures for uterine intracavitary pathology in perimenopausal and postmenopausal women with AUB. It could also help differentiate malignant diseases from benign endometrial changes.

Indirect carotid cavernous fistulas (ICCFs) are rare disease entities triggered by indirect low flow between the cavernous sinus and the meningeal branches of the internal or external carotid artery or from both. To evaluate clinical characteristics, angiography, and relationship to the result of endovascular treatment of indirect carotid cavernous fistulas. A prospective study of 32 patients diagnosed to identify ICCFs through digital subtraction angiography (DSA) and intravascular intervention. Results showed that indirect carotid cavernous fistulas were more common in females of advanced age (68.8%) and occurred spontaneously (87.5%); the most common symptoms included chemosis (93.75%), headache (87.5%), proptosis (53.1%), and tinnitus (37.5%) with type D being the most common (81.2%). Thirty out of thirty-two patients with intervention had complete flow block (83.3%), near complete (13.3%), and failure in one case. Incomplete occlusion interventions and failures were of the type D complication. The complication and number of drainage veins are related (p

The article presents a new method for diagnosing the technical condition of radio-electronic components, combining the methods of thermal diagnostics with the technologies of artificial neural networks. The structure of the method is shown, and the composition of the functional blocks is determined. The implementation of the method is a symbiosis of technologies for mathematical and simulation modeling of the technical state of a radio-electronic device with its physical tests and research of characteristics. When developing the method, specialized software tools for design and circuit design were actively used, such as Altium Designer CAD, SolidWorks, NI Multisim, the FloTHERM PCB thermal analysis module, as well as the MATLAB mathematical modeling and calculation package. With the help of these tools, a number of studies were carried out, including sets of numerical values of the power of circuit elements and temperature indicators of the printing unit, both for the correct state of the device and in states with artificially introduced defects. They, in turn, became the basis of the database of electronic node failures. To implement diagnostic procedures and identify the technical condition, an artificial neural network based on selforganizing Kohonen maps was created, its structure, parameters and algorithms of functioning were determined. The diagnostic procedure is based on the analysis of information from the fault database and its comparison with experimental data obtained as a result of a physical experiment. The results of the study showed that the network automatically classifies the characteristic defects of electronic components using the algorithms embedded in it. The list of characteristic defects in the proposed diagnostic method is limited to a discrete set of the most common faults, because, as their number increases, the use of the self-organizing Kohonen network for automatic classification becomes much more complicated and ineffective in terms of performance and reliability of identification. Among the advantages of this technology, it should be noted that the Kohonen network has the ability to convert largedimensional input data into a two-dimensional array. So, the results are easy to visualize and convenient to use when generating reports and recommendations for subsequent decision-making about the possibility of using an electronic device.

Abstract Background Postherpetic neuralgia (PHN) is the most common and bearable complication of herpes zoster (HZ). This pain may have negative impact on the patient's all aspects of daily life and health‐related quality of life (HRQOL). Despite numerous advances in treatment, many patients remain resistant to the current therapy options. It is the first time subcutaneous injection of methylprednisolone acetate and lidocaine has been used to treat refractory PHN. We report the results of this treatment evaluating pain relief and HRQOL improvement in this disorder. Methods A total of 43 patients with refractory PHN was enrolled in the observational study. All patients received daily subcutaneous injection of methylprednisolone acetate and lidocaine for 10 consecutive days. The severity of pain was assessed by using Visual Analog Scale (VAS), and 36‐Item Short Form Survey (SF‐36) was applied to evaluate HRQOL. Assessment of the pain and HRQOL was carried out at baseline and posttreatment at 4 weeks as well as 6 and 12 months. Results At baseline, all patients experienced severe PHN with average VAS scores of 8.44 ± 0.85 (minimum 7; maximum 10). At 4 weeks, 6 months, and 12 months after treatment, the pain had significantly decreased (P

Background: Mental health risk-factors for Asian migrants have been studied almost exclusively in the US, Canada, and Australia but not in European countries. Therefore, we aimed to identify sociodemographic, clinical, and migration-surrounding factors associated with experienced mental distress among Vietnamese migrants in Germany.Method: 305 Vietnamese migrants utilizing Germany's first Vietnamese psychiatric outpatient clinic filled out at admission the Brief-Symptom-Inventory 18 (BSI-18) as well as a questionnaire on 22 potential mental health determinants. Using a multiple linear regression model, we identified those sociodemographic, clinical, and migration-surrounding factors that were significantly related to the Global Severity Index (GSI) of the BSI-18.Results: The factors unemployment (B = −6.32, p = 0.014), financial problems (B = −10.71, p < 0.001), no or only little religious involvement (B = −3.23, p = 0.002), no psychiatric precontact (B = −7.35, p = 0.004), previous migration experiences (B = 8.76, p = 0.002), and perceived discrimination (B = 6.58, p = 0.011) were found to significantly increase the level of mental distress according to the BSI-GSI.Conclusion: Based on these results, we were able to construct a mental health risk-profile for Vietnamese migrants in Germany, which aims to detect candidates for psychiatric problems earlier and supply them with customized prevention and therapy options.