The effect of a 6-month walking program on biochemical parameters in sedentary adults with type 2 diabetes mellitus.

This study assessed the effect of a 6-month walking program on biochemical parameters in patients with type-2 diabetes mellitus. A group of 40 sedentary patients with type-2 diabetes volunteered to participate in this study. Plasma glucose, triglyceride, total cholesterol, low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase, urea, creatinine, uric acid, C-reactive protein (CRP), and erythrocyte sedimentation rate were measured. Differences in outcome measures between pre- and post-intervention were assessed using paired t-test or Wilcoxon signed-rank test, with effect sizes interpreted separately for normally (µ2: small 0.01-0.06, moderate 0.061-0.14, and large >0.14) and non-normally distributed data (r: small = 0.1-0.3, moderate = 0.3-0.5, and large >0.5). Significant (p < 0.001) large decrease between the initial and final measurements was observed for glucose (r = 0.62), total cholesterol (µ2 = 0.88), triglycerides (r = 0.62), LDL (r = 0.61), AST (µ2 = 0.82), ALT (µ2 = 0.79), gamma-glutamyl transferase (µ2 = 0.79), urea (µ2 = 0.92), creatinine (r = 0.62), uric acid (r = 0.62), CRP (µ2 = 0.80), and erythrocyte sedimentation rate (µ2 = 0.58). On the other hand, significant (p < 0.001) large increase between the initial and final measurements was observed for HDL (r = 0.62). Supervised 6-month aerobic walking program is an effective strategy in (1) reducing hyperglycemia; (2) increasing HDL and reducing LDL, and triglycerides; (3) reducing plasma biomarkers of liver dysfunction, kidney dysfunction, and inflammation in type-2 diabetic patients.

The HEV Ventilator: at the interface between particle physics and biomedical engineering.

A high-quality, low-cost ventilator, dubbed HEV, has been developed by the particle physics community working together with biomedical engineers and physicians around the world. The HEV design is suitable for use both in and out of hospital intensive care units, provides a variety of modes and is capable of supporting spontaneous breathing and supplying oxygen-enriched air. An external air supply can be combined with the unit for use in situations where compressed air is not readily available. HEV supports remote training and post market surveillance via a Web interface and data logging to complement standard touch screen operation, making it suitable for a wide range of geographical deployment. The HEV design places emphasis on the ventilation performance, especially the quality and accuracy of the pressure curves, reactivity of the trigger, measurement of delivered volume and control of oxygen mixing, delivering a global performance which will be applicable to ventilator needs beyond the COVID-19 pandemic. This article describes the conceptual design and presents the prototype units together with a performance evaluation.

Floating-Gate MOS Transistor with Dynamic Biasing as a Radiation Sensor.

This paper describes the possibility of using an Electrically Programmable Analog Device (EPAD) as a gamma radiation sensor. Zero-biased EPAD has the lowest fading and the highest sensitivity in the 300 Gy dose range. Dynamic bias of the control gate during irradiation was presented for the first time; this method achieved higher sensitivity compared to static-biased EPADs and better linear dependence. Due to the degradation of the transfer characteristics of EPAD during irradiation, a function of the safe operation area has been found that determines the maximum voltage at the control gate for the desired dose, which will not lead to degradation of the transistor. Using an energy band diagram, it was explained why the zero-biased EPAD has higher sensitivity than the static-biased EPAD.

A Modified Distributed Bees Algorithm for Multi-Sensor Task Allocation.

Multi-sensor systems can play an important role in monitoring tasks and detecting targets. However, real-time allocation of heterogeneous sensors to dynamic targets/tasks that are unknown a priori in their locations and priorities is a challenge. This paper presents a Modified Distributed Bees Algorithm (MDBA) that is developed to allocate stationary heterogeneous sensors to upcoming unknown tasks using a decentralized, swarm intelligence approach to minimize the task detection times. Sensors are allocated to tasks based on sensors' performance, tasks' priorities, and the distances of the sensors from the locations where the tasks are being executed. The algorithm was compared to a Distributed Bees Algorithm (DBA), a Bees System, and two common multi-sensor algorithms, market-based and greedy-based algorithms, which were fitted for the specific task. Simulation analyses revealed that MDBA achieved statistically significant improved performance by 7% with respect to DBA as the second-best algorithm, and by 19% with respect to Greedy algorithm, which was the worst, thus indicating its fitness to provide solutions for heterogeneous multi-sensor systems.

Distributed bees algorithm parameters optimization for a cost efficient target allocation in swarms of robots.

Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the distributed bees algorithm (DBA), previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBA's control parameters by means of a genetic algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robots' distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce.