Harmonic scalpel versus titanium clips and l-hook in the ligation of cystic duct and artery and gall bladder dissection in laparoscopic cholecystectomy

Background: Laparoscopic cholecystectomy is a gold standard for gall bladder stone surgery. The Aim and objective of study was to compare the total duration of surgery, intraoperative complication like bile leak from cystic duct stump, spillage of bile from gallbladder and post op pain and abdomen distension and jaundice.Methods: Study was carried out in dept. of gen Surgery, Govt medical college Kota in yr. 2015-16 in a total of 50 patients with cholelithiasis with cholecystitis. Patient were equally divided randomly into two groups (a) Harmonic scalpel group and (b) Titanium Clip and L hook group. All patients with medical comorbidities, Concomitant CBD calculi, cirrhosis and portal HT were excluded from study. Intraoperatively adhesions, bile spillage from GB and cystic duct stump noted Postoperatively complain like pain abdomen, Jaundice, and fever were noted. Duration of hospital stay was observed. All results were statistically analyzed using Chi square and ANOVA test.Results: Both groups were comparable on the basis of age and sex distribution, as no statistically difference was noted (P value 0.867 and 0.999 respectively). Intraoperative findings were adhesions 5 in clip group and 7 in harmonic group. Spillage from gall bladder was 2 in Clip group and 3 in harmonic group. Mean duration of surgery was 65.20 min in clip group and 63.68 in harmonic group with no statistically significant difference in both the group (P Value 0.727). Average duration of hospital stay was similar in both the groups with a mean of 2.6 days. Postoperative complication was fever, abdomen pain and distension were 3,1,1 were respectively in the clip group and 3,2,2 respectively in harmonic group with the P value of 0.999 which was statistically insignificant. No CBD injury was noted in any case. Conversion to open cholecystectomy was not done in any case. On 1week and 1 month follow up 2 cases in clip group and 1 in HS group had collection in gall bladder fossa and none at I month.Conclusions: Harmonic scalpel offers an effective, alternative and safe method to cystic duct division and Gallbladder dissection from liver bed

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 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

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

Development of Nickel Based Multifunctional Additive & Performance Evaluation of Photo Biodegradation

Nickel Based Multifunctional additive (Ni-MFA) different compositions (1, 2 & 3 wt %) of Ni-MFA Electron Microscopy (SEM), mechanical properties by Differential Scanning Calorimetry (DSC). incubated in the presence of the microbes such as asspergillus dump. Both living organism were capable of degrading polypropylene. was biodegraded within 45 days, %