Finite Element Analysis of Load Bearing Capacity of a Reinforced Concrete Frame Subjected to Cyclic Loading

Many methods have been developed in order to study the impact behavior of solids and structures. Two common methods are finite element and experimental method. The nonlinear finite element method is one the most effective methods of predicting the behavior of RC beams from zero-load to failure and its fracture, yield and ultimate strengths. The advantage of this method is its ability to make this prediction for all sections of the assessed RC beam and all stages of loading. This paper compares the experimental results obtained for a RC frame with the numerical results calculated by ABAQUS software, and plots both sets of results as hysteresis–displacement diagrams. This comparison shows that the numerical FEM implemented via ABAQUS software produce valid and reliable results for load bearing capacity of RC frames subjected to cyclic loads, and therefore has significant cost and time efficiency advantages over the alternative approac

Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques.

The main objective of this work is to establish a framework for processing and evaluating the lower limb electromyography (EMG) signals ready to be fed to a rehabilitation robot. We design and build a knee rehabilitation robot that works with surface EMG (sEMG) signals. In our device, the muscle forces are estimated from sEMG signals using several machine learning techniques, i.e. support vector machine (SVM), support vector regression (SVR) and random forest (RF). In order to improve the estimation accuracy, we devise genetic algorithm (GA) for parameter optimisation and feature extraction within the proposed methods. At the same time, a load cell and a wearable inertial measurement unit (IMU) are mounted on the robot to measure the muscle force and knee joint angle, respectively. Various performance measures have been employed to assess the performance of the proposed system. Our extensive experiments and comparison with related works revealed a high estimation accuracy of 98.67% for lower limb muscles. The main advantage of the proposed techniques is high estimation accuracy leading to improved performance of the therapy while muscle models become especially sensitive to the tendon stiffness and the slack length. Graphical Abstract

In Silico Analysis of Neutralizing Antibody Epitopes on The Hepatitis C Virus Surface Glycoproteins

Objective: Despite of antiviral drugs and successful treatment, an effective vaccine against hepatitis C virus (HCV)infection is still required. Recently, bioinformatic methods same as prediction algorithms, have greatly contributed tothe use of peptides in the design of immunogenic vaccines. Therefore, finding more conserved sites on the surfaceglycoproteins (E1 and E2) of HCV, as major targets to design an effective vaccine against genetically different virusesin each genotype was the goal of the study. Materials and Methods: In this experimental study, 100 entire sequences of E1 and E2 were retrieved from the NCBIwebsite and analyzed in terms of mutations and critical sites by Bioedit 7.7.9, MEGA X software. Furthermore, HCV-1asamples were obtained from some infected people in Iran, and reverse transcriptase-polymerase chain reaction (RTPCR)assay was optimized to amplify their E1 and E2 genes. Moreover, all three-dimensional structures of E1 andE2 downloaded from the PDB database were analyzed by YASARA. In the next step, three interest areas of humoralimmunity in the E2 glycoprotein were evaluated. OSPREY3.0 protein design software was performed to increase theaffinity to neutralizing antibodies in these areas. Results: We found the effective in silico binding affinity of residues in three broadly neutralizing epitopes of E2glycoprotein. First, positions that have substitution capacity were detected in these epitopes. Furthermore, residuesthat have high stability for substitution in these situations were indicated. Then, the mutants with the strongest affinityto neutralize antibodies were predicted. I414M, T416S, I422V, I414M-T416S, and Q412N-I414M-T416S substitutionstheoretically were exhibited as mutants with the best affinity binding. Conclusion: Using an innovative filtration strategy, the residues of E2 epitopes which have the best in silico bindingaffinity to neutralizing antibodies were exhibited and a distinct peptide library platform was designed

Design of elbow rehabilitation device based on cable-actuated mechanism

In this paper, a cable actuated mechanism is introduced as a new rehabilitation method. Having the need for a rehabilitation system consistent with the physical characteristics has led to certain new projects. Considering joint compliance during the motion can make the patient feel better and thus, bring success for the rehabilitation program. The advantages of the proposed low-cost and light weight mechanism are having a smooth joint motion with a stiffness which is adjustable, compensate gravity effects, remote actuation for delivering torque to a joint, motor size reduction and in the developed versions, ability to control multiple joints movements and deliver force to links with one source of power. In this paper, the joints torque and stiffness is investigated in the performance of the elbow rehabilitation device. This is effectively compatible with the elbow joint stiffness. Being similar to the behavior of human arm, the mechanism can be used more widely in the field of medical robotics

Computational Control Strategy for Reducing Medial Compartment Load in Knee Bracing with Embedded Actuator

Medial unloader braces represent a primary noninvasive approach for alleviating knee pain. However, conventional valgus unloader braces, while reducing load on the medial compartment, inadvertently increase load on the lateral compartment through rotation from adduction to abduction. This phenomenon significantly elevates the risk of damage to the lateral compartment. To address this issue, we introduce a novel embedded actuation mechanism that unloads the knee using a pioneering computational procedure. By considering the knee osteoarthritis condition, we propose the calculation of the adduction knee angle and cartilage penetration depth as surrogate parameters for assessing knee pain. Accordingly, the newly developed unloader brace redistributes the load by precisely correcting the abduction angle. Additionally, we determine the maximum required torque for effectively tracking the desired abduction angle. Then, the saturated torque through the robust control method is applied in the presence of interaction force uncertainty between the orthosis and the user. A very small femur rotation change (1.7°) from adduction to abduction in the frontal plane is adequate to significantly reduce the medial contact force (around 886 N). The required robust external abduction torque is determined to be 27.6 Nm. The result shows that the novel procedure and brace prevent excessive overloading of the lateral compartment while it unloads the medial compartment sufficiently. This innovative approach offers significant potential for optimizing unloader brace design and enhancing the management of knee osteoarthritis

Joint mechanical properties estimation with a novel EMG-based knee rehabilitation robot: A machine learning approach

Joint dynamic properties play essential roles in a wide range of biomechanical movement control. This paper develops a device with a novel mechatronic design to apply small-amplitude perturbations to the human knee. Surface Electromyography is employed to record such information; at the same time, force and position sensors collect measurements to be sent to identify human joint dynamics. For classification and estimation of force, support vector machine and support vector regression techniques are applied, respectively. We devise a genetic algorithm for parameter optimization and feature extraction within the proposed methods to improve the estimation accuracy. These are then analyzed and compared to the output of our estimation model to provide a reliable comparison. Our extensive experimental results reveal a high estimation accuracy for lower limb muscles to regulate robot impedance parameters. Although the identification method sounds similar to traditional ones, knee joint properties can be estimated by the machine learning approach from the surface Electromyography without perturbations

The Effect of Radioactive Iodine (Iodine 131) on the Parameters of Sperm in Adult Male Rats

Background and Objectives: Humans live in the world of the waves and energies; the waves that are emitted from various sources and are harmful. One of the possible side effects of radioactive substances on the body is its effect on the amount of sperm production and fertility. In the present study, the effect of iodine 131, was investigated on the motility and number of sperm in male rat.   Methods: In this experimental study, 40 adult male rats were divided into two groups: Treatment group treated with oral gavage of iodine 131 and control group. After 24 hours, the number and motility of sperms in both groups, were analyzed by T statistical test.   Results: In this study, there was a significant difference in motility and number of sperm between the iodine 131 treatment group and the control group. Moreover, the number of active progressive and dead immotile sperm in the group treated with iodine 131, respectively, showed significant decrease and increase compared to the control group, but, there was no significant difference between the two groups in the less motile and non-progressive sperms. Conclusion: According to the results of this study, considering iodine 131 is used in the treatment of various diseases, thus, treatment with this method can have harmful effects on male reproductive system, such as motility and sperm count

مروری بر کاربردهای سیستم BCI در علم توان‌بخشی با رویکرد بازتوانی ارتباط با محیط پیرامون

چکیده   مقدمه: مشکلاتی که پس از ضربه مغزی یا بیماری‌های دستگاه عصبی رخ می‌دهد منجر به بروز محدودیت‌های حرکتی و کلامی برای مدت طولانی در افراد می‌شود. پیشرفت‌های صورت گرفته در زمینه ارتباط مغز انسان و کامپیوتر (BCI) امکان شناسایی و طبقه‌بندی فعالیت‌های الکتریکی و متابولیک مغز و تبدیل آنها به یک فرمان کنترلی برای کامپیوتر و یا یک دستگاه مخصوص را فراهم می‌نماید. هدف از مطالعه حاضر مروری بر کاربردهای سیستم BCI در علم توان‌بخشی با رویکرد بازتوانی ارتباط با محیط پیرامون بود. مواد و روش‌ها: هدف استفاده از سیستم BCI به طور کلی یا ایجاد یک توانایی از دست رفته در فرد و یا بهبود توانایی تحلیل رفته می‌باشد. بر همین اساس سه کاربرد عمده برای سیستم BCI عبارت است از ایجاد امکان حرکت اعضای بدن، ایجاد قدرت تکلم و کنترل تجهیزات مختلف به منظور انجام فعالیت‌های روزانه. برای بررسی پیشرفت‌های صورت گرفته در این عرصه‌ها مقالات مرتبط با موضوع که تاکنون در مجلات و کنفرانس‌های معتبر ارایه گردیده مطالعه شده است. یافته‌ها: مفاهیم و اصول BCI به همراه فن‌آوری‌های مورد استفاده در این عرصه و آخرین پیشرفت‌ها در زمینه بهبود عملکرد سیستم‌های BCI در این مقاله ارایه شده است. در انتها نیز ظرفیت‌های موجود برای استفاده از سیستم  BCIو کارهای مورد نیاز برای توسعه بیشتر استفاده از آن در توان‌بخشی مورد بحث قرار گرفته است. نتیجه‌گیری: در 20 سال اخیر تلاش‌های بسیاری به منظور افزایش راندمان و سرعت انتقال داده در سیستم های BCI مبتنی بر EEG صورت گرفته است. برای دستیابی به این اهداف یک سیستم تبادل داده پرسرعت بین مغز و کامپیوتر مورد نیاز می‌باشد. تاکنون راهکارهای گوناگونی برای ایجاد یک ارتباط بدون تاخیر بین دستورات مغزی صادر شده و فرامین کنترلی تولید شده برای دستگاه‌ها معرفی شده است. کلید‌واژه‌ها: سیگنال‌های الکتروانسفالوگرافی، واسط مغز و کامپیوتر، توان‌بخش

Decentralized Implementation of Unit Commitment with Analytical Target Cascading: A Parallel Approach

This paper presents a decentralized solution algorithm for network-constrained unit commitment (NCUC) in multiregional power systems. The proposed algorithm is based on our previous work in which a local NCUC was formulated for each control entity (i.e., region) and an analytical target cascading (ATC) based distributed but partially parallelized algorithm requiring a central coordinator was presented. The primary objective of this paper is to present a decentralized approach that relaxes the need for any form of central coordinator in ATC and allows fully parallelized solutions of the local NCUCs. To achieve this objective, we formulate a bilevel optimization problem for each control entity. While the upper level solves the NCUC problem of the control entity, the lower level seeks to coordinate the control entity with its neighboring regions. The lower level is a convex optimization, which can be further replaced in the upper level problem by the Karush-Kuhn-Tucker conditions. The control entities communicate directly with each other and synchronously solve their local NCUCs. Having no need for any form of central coordinator, the proposed algorithm is potentially less vulnerable to cyber-attacks and communication failures than the distributed methods utilizing a coordinator