Wonki Lee

Effects of antirheumatic therapy on serum lipid levels in patients with rheumatoid arthritis: A prospective study

BACKGROUND Patients with newly diagnosed rheumatoid arthritis have adverse serum lipid profiles. We sought to determine the effects of treating rheumatoid arthritis with antirheumatic drugs on these abnormal lipid levels. SUBJECTS AND METHODS We studied 42 patients with newly diagnosed rheumatoid arthritis who had not been treated with corticosteroids or disease-modifying antirheumatic drugs. We measured serum lipid profiles at baseline and 1 year later, and determined whether there were differences in the changes in lipid levels between patients who met the American College of Rheumatology criteria for a 20% improvement in rheumatoid arthritis and those who did not. RESULTS Of the 42 patients, 27 (64%) met the criteria for a 20% improvement in rheumatoid arthritis during the 12-month study. In these patients, mean high-density lipoprotein (HDL) cholesterol levels increased by 21% (P <0.001), apolipoprotein A-I levels increased by 23% (P <0.001), and the ratio of low-density lipoprotein (LDL) cholesterol to HDL cholesterol level decreased by 13% (P = 0.10). There were significant between-group differences (responders-nonresponders) in the mean 12-month changes in HDL cholesterol levels (8.0 mg/dL; 95% confidence interval [CI]: 3 to 13 mg/dL; P = 0.002), apolipoprotein A-I levels (21 mg/dL; 95% CI: 8 to 33 mg/dL; P = 0.003), and the LDL cholesterol to HDL cholesterol ratio (-0.6; 95% CI: -0.1 to -1.0; P = 0.03), but not in LDL cholesterol, apolipoprotein B-100, or lipoprotein(a) levels. CONCLUSION Active rheumatoid arthritis is associated with an adverse lipid profile that improves substantially following effective treatment of rheumatoid arthritis. This improvement may reduce the risk of cardiovascular disease.

Adaptive Synchronization of Discrete-Time T-S Fuzzy Chaotic Systems Using Output Tracking Control

This paper presents a model reference adaptive control approach for the synchronization of a discrete-time chaotic systems using output tracking control. Takagi-Sugeno (T-S) fuzzy model is employed to represent the discrete-time chaotic system. Then, the reference model system is chosen using the output of master system and design the control input so that the controlled slave system achieves asymptotic synchronization with the reference system given that two systems start from different initial conditions, different parameters and/or different type of model. Using a gradient algorithm, the ideal controller gains which can stabilize the error equation are estimated. Simulation examples of two cases are given to demonstrate the validity of our proposed adaptive method

Adaptive Division of Labor in Multi-Robot System with Minimum Task Switching

In many multi-robot systems, various tasks are allocated dynamically to an individual robot and each robot should decide its own work that is the best commensurate with its current state. To solve complex task allocation problems, agentbased approaches based on the model of division of labor of many social insects have gained increasing attentions in recent years. In this paper, we consider the problem of adjusting the ratio of robots equally to the ratio of given tasks to handle the division of labor dynamically with less number of task switches. Inspired by several insect societies displaying an effective division of labor with the limited abilities, the response threshold model is applied. An Individual robot has a limited, constant-sized task queue and the information obtained from the observation behavior is stored within this queue. Using the ratio of tasks in queue and the predefined response threshold values for all possible tasks, an individual agent decide its task and to handle the desired division of labor dynamically and obtains the specialization for the specific tasks that induces the less number of task switching. To show the robustness and flexibility of our proposed method, various experiments are executed and the results are compared with an other method.

History-based response threshold model for division of labor in multi-agent systems

Dynamic task allocation is a necessity in a group of robots. Each member should decide its own task such that it is most commensurate with its current state in the overall system. In this work, the response threshold model is applied to a dynamic foraging task. Each robot employs a task switching function based on the local task demand obtained from the surrounding environment, and no communication occurs between the robots. Each individual member has a constant-sized task demand history that reflects the global demand. In addition, it has response threshold values for all of the tasks and manages the task switching process depending on the stimuli of the task demands. The robot then determines the task to be executed to regulate the overall division of labor. This task selection induces a specialized tendency for performing a specific task and regulates the division of labor. In particular, maintaining a history of the task demands is very effective for the dynamic foraging task. Various experiments are performed using a simulation with multiple robots, and the results show that the proposed algorithm is more effective as compared to the conventional model.

Handling interference effects on foraging with bucket brigades

Many kinds of bio-inspired tasks have been tested with swarm robotics and task partitioning is one of the challenging subjects. In nature, it is well known that some colonies of social insects such as honeybees, termites, and ants use task partitioning strategies for their survival. In this paper, we demonstrate an effect of the task partitioning strategy called bucket brigade, which uses the direct transfer of materials or food between a pair of workers. We propose a task partitioning strategy based on the moving speeds of agents for the foraging task. We test various environmental conditions and compare the performance between task partitioning groups and non-partitioning groups. The experimental results show that task partitioning may not always be the best solution for foraging performance. However, when there exists a transfer bottleneck at a central location such as the entrance of the nest, task partitioning can be an effective strategy for reducing the traffic jam and improving the overall foraging performance of a group. The bucket brigade sequenced from the slowest agents (near the food source) to the fastest agents (near the nest) can particularly improve performance significantly in the region with traffic congestion near the nest. Generally, many social insect colonies consist of a number of members, and the entrances of colony nests always suffer from heavy traffic congestion. Our experimental results support the hypothesis that several social insects use one of the task partitioning strategies based on bucket brigades in their foraging tasks.

Autonomous Shepherding Behaviors of Multiple Target Steering Robots

This paper presents a distributed coordination methodology for multi-robot systems, based on nearest-neighbor interactions. Among many interesting tasks that may be performed using swarm robots, we propose a biologically-inspired control law for a shepherding task, whereby a group of external agents drives another group of agents to a desired location. First, we generated sheep-like robots that act like a flock. We assume that each agent is capable of measuring the relative location and velocity to each of its neighbors within a limited sensing area. Then, we designed a control strategy for shepherd-like robots that have information regarding where to go and a steering ability to control the flock, according to the robots’ position relative to the flock. We define several independent behavior rules; each agent calculates to what extent it will move by summarizing each rule. The flocking sheep agents detect the steering agents and try to avoid them; this tendency leads to movement of the flock. Each steering agent only needs to focus on guiding the nearest flocking agent to the desired location. Without centralized coordination, multiple steering agents produce an arc formation to control the flock effectively. In addition, we propose a new rule for collecting behavior, whereby a scattered flock or multiple flocks are consolidated. From simulation results with multiple robots, we show that each robot performs actions for the shepherding behavior, and only a few steering agents are needed to control the whole flock. The results are displayed in maps that trace the paths of the flock and steering robots. Performance is evaluated via time cost and path accuracy to demonstrate the effectiveness of this approach.

Local Interaction of Agents for Division of Labor in Multi-agent Systems

Task allocation problem has been an issue in multi-agent systems. Among many interesting tasks, we focus on an algorithm for the proportional regulation of population where the swarm is divided into groups depending on task demands. We take the response threshold model inspired by division of labor in several social insects. In our approach, the member proportion of each sub-group is regulated proportional to the external task demands and local social interactions among agents. Here, the interactions control the response thresholds for given tasks. The proposed algorithm was applied to simulation experiments of robots, and the experimental results show that the proposed method has adaptive and robust responses under dynamically changing environments.

Dynamic Task Allocation Using a Pheromone-Based Approach in Factory Domain Applications

The scheduling problem in real factory manufacturing systems is comprised of number of parallel machines. Each machine is capable of processing several tasks, but it may need extra costs if the current machine state should be changed to perform a different task with the current performing task. In that case, minimizing such changes with maintaining some desired performance is recommended for maximizing the overall system performance. This paper concerns a dynamic scheduling problem and scheduling algorithm is proposed based on agent based approaches inspired from division of labor in several social insects. Depending on the stimulus of task and the corresponding threshold value, individual agent will or will not perform task. The appropriate threshold for an effective scheduling is obtained by pheromone-based approach that uses the information about processed tasks in each individual agent, and the simulation results show that the performance of the proposed approach is comparable with other conventional methods.

The revaluation of a single low dose of streptomycin powder in chemical labyrinthectomy for ménière's disease: Our experience in ninety‐eight patients

• We present the outcomes of adenotonsillectomy for sleep disordered breathing in 142 children under 3 years of age without onsite paediatric intensive care unit facilities. • All patients in this age group are given 5 days of preoperative oral dexamethasone and antibiotics. • Our results have shown that no patients required a higher level of respiratory support postoperatively nor transfer to another unit, and only one patient required a return to theatre with a post-tonsillectomy haemorrhage. • Theaimofadministeringoral steroidsandantibiotics is to reduce adenotonsillar hypertrophy, airway inflammation infective rhinosinusitis andchronic cough, thus correcting central respiratory stimulatory pathways. • A double-blinded randomised control trial is needed to test our proposal that our preoperative regimen reduces adverse outcomes in this cohort of patients.

Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns

The electrocardiogram (ECG) waveform conveys information regarding the electrical property of the heart. The patterns vary depending on the individual heart characteristics. ECG features can be potentially used for biometric recognition. This study presents a new method using the entire ECG waveform pattern for matching and demonstrates that the approach can potentially be employed for individual biometric identification. Multi-cycle ECG signals were assessed using an ECG measuring circuit, and three electrodes can be patched on the wrists or fingers for considering various measurements. For biometric identification, our-fold cross validation was used in the experiments for assessing how the results of a statistical analysis will generalize to an independent data set. Four different pattern matching algorithms, i.e., cosine similarity, cross correlation, city block distance, and Euclidean distances, were tested to compare the individual identification performances with a single channel of ECG signal (3-wire ECG). To evaluate the pattern matching for biometric identification, the ECG recordings for each subject were partitioned into training and test set. The suggested method obtained a maximum performance of 89.9% accuracy with two heartbeats of ECG signals measured on the wrist and 93.3% accuracy with three heartbeats for 55 subjects. The performance rate with ECG signals measured on the fingers improved up to 99.3% with two heartbeats and 100% with three heartbeats of signals for 20 subjects.