Embedment Effect on Foundations under Vertical Vibrations

The dynamic response of the embedded foundation subjected to vertical dynamic loads has been studied through carefully conducted field tests. The block was excited in vertical and coupled modes of vibrations. Four excitation levels were used. Tests, with different embedment depths were carried out. The foundation block was instrumented to monitor dynamic contact pressure at various embedments with specially designed contact pressure cells. Side shear resistances were measured through dynamic shear resistance cells specially designed for the purpose. Also, frequency amplitude characteristics were observed during each test .The analysis of data indicates that as the depth of embedment increases, damping factor, stiffness and in-phase soil mass increase. Dynamic pressure distributions exhibit marked changes with embedment depth. The dynamic shear resistances developed on the vertical side surfaces, vary non-linearly

Human lower limb activity recognition techniques, databases, challenges and its applications using sEMG signal: an overview

Human lower limb activity recognition (HLLAR) has grown in popularity over the last decade mainly because to its applications in the identification and control of neuromuscular disorders, security, robotics, and prosthetics. Surface electromyography (sEMG) sensors provide various advantages over other wearable or visual sensors for HLLAR applications, including quick response, pervasiveness, no medical monitoring, and negligible infection. Recognizing lower limb activity from sEMG signals is also challenging owing to the noise in the sEMG signal. Pre- processing of sEMG signals is extremely desirable before the classification because they allow a more consistent and precise evaluation in the above applications. This article provides a segment-by-segment overview of: (1) Techniques for eliminating artifacts from sEMG signals from the lower limb. (2) A survey of existing datasets of lower limb sEMG. (3) A concise description of the various techniques for processing and classifying sEMG data for various applications involving lower limb activity. Finally, an open discussion is presented, which may result in the identification of a variety of future research possibilities for human lower limb activity recognition. Therefore, it is possible to anticipate that the framework presented in this study can aid in the advancement of sEMG-based recognition of human lower limb activity

Rapid symptom control in neuroleptic malignant syndrome with electroconvulsive therapy: A case report

Introduction: Neuroleptic malignant syndrome (NMS), thought to arise through dopamine antagonism, is life-threatening. While prompt diagnosis of NMS is critical, it may be obscured by other diagnoses, such as malignant catatonia, with overlapping, life-threatening symptoms. Initiation of dopamine-blocking agents such as antipsychotics and abrupt cessation of dopaminergic medications such as amantadine can precipitate NMS. Once NMS is suspected, deft medical management should ensue. Multiple case reports detail electroconvulsive therapy's (ECT's) effectiveness in the treatment of NMS. While this relationship is well-documented, there is less literature regarding comparative efficacy of ECT in the acute treatment of NMS-like states precipitated by withdrawal of dopamine agonists, such as amantadine. Case: We present a 52-year-old female with schizoaffective disorder bipolar type, with a history of a lorazepam-resistant catatonic episode the prior year that had responded to amantadine. She presented febrile with altered mental status, lead pipe rigidity, mutism, grasp reflex, stereotypy, autonomic instability, and a Bush-Francis Catatonia Rating Scale (BFCRS) of 24, suggesting malignant catatonia versus NMS. There was concern over a potentially abrupt cessation of her amantadine of which she had been prescribed for the past year. Interventions: Organic etiologies were ruled out, and a presumptive diagnosis of NMS was made with central dopaminergic depletion from abrupt dopamine agonist (amantadine) withdrawal as the suspected underlying etiology. After intravenous lorazepam and reinduction of amantadine failed to alleviate her symptoms, urgent ECT was initiated. Our patient received an index series of ECT of seven treatments. After ECT #1 she was no longer obtunded, after treatment #2 her symptoms of mutism, rigidity, stereotypy, and agitation showed improvement, and by ECT #3, the NMS had rapidly dissipated as evidenced by stable vital signs, lack of rigidity, and coherent conversation. Conclusion: Brisk identification of potentially life-threatening NMS and NMS-like states, including malignant catatonia, warrants a trial of ECT. ECT's theoretical mechanisms of action coincide with the theoretical pathophysiology of the conditions. It is a viable and safe treatment option for reducing mortality. With prompt initiation of ECT, we obtained rapid control of a condition with a potentially high mortality.info:eu-repo/semantics/publishedVersio

Comparative Analysis of Machine Learning Techniques for the Classification of Knee Abnormality

Knee abnormality is a major problem in elderly people these days. It can be diagnosed by using Magnetic Resonance Imaging (MRI) or X-Ray imaging techniques. X-Ray is only used for primary evaluation, while MRI is an efficient way to diagnose knee abnormality, but it is very expensive. In this work, Surface EMG (sEMG) signals acquired from healthy and knee abnormal individuals during three different lower limb movements: Gait, Standing and Sitting, were used for classification. Hence, first Discrete Wavelet Transform (DWT) was used for denoising the input signals; then, eleven different time-domain features were extracted by using a 256 msec windowing with 25% of overlapping. After that, the features were normalized between 0 (zero) to 1 (one) and then selected by using the backward elimination method based on the p-value test. Five different machine learning classifiers: K-nearest neighbor, support vector machine, decision tree, random forest and extra tree, were studied for the classification step. Our result shows that the Extra Tree Classifier with ten cross-validations gave the highest accuracy (91%) in detecting knee abnormality from the sEMG signals under analysis. (c) 2020 IEEE

Human knee abnormality detection from imbalanced sEMG data

The classification of imbalanced datasets, especially in medicine, is a major problem in data mining. Such a problem is evident in analyzing normal and abnormal subjects about knee from data collected during walking. In this work, surface electromyography (sEMG) data were collected during walking from the lower limb of 22 individuals (11 with and 11 without knee abnormality). Subjects with a knee abnormality take longer to complete the walking task than healthy subjects. Therefore, the SEMG signal length of unhealthy subjects is longer than that of healthy subjects, resulting in a problem of imbalance in the collected sEMG signal data. Thus, the development of a classification model for such datasets is challenging due to the bias towards the majority class in the data. The collected sEMG signals are challenging due to the contribution of multiple motor units at a time and their dependency on neuromuscular activity, physiological and anatomical properties of the involved muscles. Hence, automated analysis of such sEMG signals is an arduous task. A multi-step classification scheme is proposed in this research to overcome this limitation. The wavelet denoising (WD) scheme is used to denoise the collected sEMG signals, followed by the extraction of eleven time-domain features. The oversampling techniques are then used to balance the data under analysis by increasing the training minority class. The competency of the proposed scheme was assessed using various computational classifiers with 10 fold cross-validation. It was found that the oversampling techniques improve the performance of all studied classifiers when applied to the studied imbalanced sEMG data. (c) 2021 Elsevier Lt

Operation of Circuit Breaker with the help of Password

A circuit breaker is an electrical switch use to protect an electrical circuit from damage caused by faults. Its basic function is to detect a fault condition and protect from it. Fuse operates once after that it must be replaced but a circuit breaker can be reset to resume normal condition. During the manual operation, we see inoperable electrical accidents to the line man are rises during maintenance due to improper communication between the maintenance staff and the substation staff. In order to prevent such accidents, password based circuit breaker is design so that only authentic person can operate it with a password. There is also a facility of changing the password. The system is fully controlled by the microcontroller. The password is saved in an EEPROM, interfaced to the microcontroller and the password can be changed any time. A keypad is used to submit the password and a relay to operate circuit breaker, which is indicated by a bulb. Any wrong attempt to open the circuit breaker by entering the wrong password an alert will be shown in the LCD