DESIGN AND SIMULATION OF A NERVE TRACER SYSTEM FOR VAGOTOMY SURGERY

The vagus nerve is one of the most important nerves in the human body. It is associated with a plethora of functions one of them being acid secretion inside the stomach to aid in digestion. The cerebrum part of the brain initiates an action potential that is propagated along the vagus nerve to the parietal cells that secrete acid. In some cases, the cerebrum over stimulates the parietal cells leading to excess acid secretion, more than is needed for the digestion process. This excess acid leads to the formation of open sores along the stomach lining called gastric/peptic ulcers. In some cases, these gastric ulcers are curable by taking medicines. In other cases, medicines have little or no effect on these gastric ulcers and a surgical intervention, called a vagotomy is recommended to cure the gastric ulcers. Vagotomy is the surgical cutting of the vagus nerve branches that link the brain to the parietal cells that produce acid. Once a branch is cut, the signal from the cerebrum is blocked and acid production is reduced thereby reducing the formation of ulcers. Vagotomy surgery can be challenging to perform. The major hurdle facing surgeons is locating the vagus nerve branch responsible for excess acid secretion in a specific zone of the stomach. If the surgeons are unable to locate the correct branch or cut other nerve branches, it will not cure the gastric ulcer problem and the entire surgical exercise may need to be revised. Thus there is a need to develop a system that would help surgeons locate and identify the correct vagus nerve branch to cut during the vagotomy surgery. A system that artificially excites the vagus nerve and provides the surgeon feedback that the laparoscopic tool is near the vagus nerve branch of interest is proposed and designed. To facilitate the design process, an external electrical stimulation model of a human vagus nerve was developed using COMSOL Multiphysics simulation software. The nerve model is built in the simulation software using the approximate geometric and material properties of the human vagus nerve. The model recapitulates the salient feature that if an applied electric potential exceeds a threshold potential, it leads to the generation of an action potential that propagates through the length of the vagus nerve. The proposed vagus nerve tracer consists of a stimulation cuff to inject a trace signal into the vagus nerve and a receiver probe that can be placed near a nerve to detect the presence of the trace signal. The stimulation cuff is a set of copper electrodes that would be placed around the vagus nerve at a point above the stomach where the vagus nerve is clearly visible and accessible to the surgeons. The detector probe is designed as copper hook monopolar tip that could be affixed to a laparoscopic instrument. It can be placed around the vagus nerve branch without damaging it and can detect the action potential. An important third component is the square wave used as the trace signal. The developed system thus comprises the vagus nerve artificial electrical stimulation cuff, trace signal, and detector probe. Computer simulations have been performed to optimize the proposed design and to demonstrate its functionality and potential value to help surgeons overcome the complication of locating the correct branch of vagus nerve to cut during the vagotomy surgery

Internet of Things and Intelligent Technologies for Efficient Energy Management in a Smart Building Environment

Internet of Things (IoT) is attempting to transform modern buildings into energy efficient, smart, and connected buildings, by imparting capabilities such as real-time monitoring, situational awareness and intelligence, and intelligent control. Digitizing the modern day building environment using IoT improves asset visibility and generates energy savings. This dissertation provides a survey of the role, impact, and challenges and recommended solutions of IoT for smart buildings. It also presents an IoT-based solution to overcome the challenge of inefficient energy management in a smart building environment. The proposed solution consists of developing an Intelligent Computational Engine (ICE), composed of various IoT devices and technologies for efficient energy management in an IoT driven building environment. ICE’s capabilities viz. energy consumption prediction and optimized control of electric loads have been developed, deployed, and dispatched in the Real-Time Power and Intelligent Systems (RTPIS) laboratory, which serves as the IoT-driven building case study environment. Two energy consumption prediction models viz. exponential model and Elman recurrent neural network (RNN) model were developed and compared to determine the most accurate model for use in the development of ICE’s energy consumption prediction capability. ICE’s prediction model was developed in MATLAB using cellular computational network (CCN) technique, whereas the optimized control model was developed jointly in MATLAB and Metasys Building Automation System (BAS) using particle swarm optimization (PSO) algorithm and logic connector tool (LCT), respectively. It was demonstrated that the developed CCN-based energy consumption prediction model was highly accurate with low error % by comparing the predicted and the measured energy consumption data over a period of one week. The predicted energy consumption values generated from the CCN model served as a reference for the PSO algorithm to generate control parameters for the optimized control of the electric loads. The LCT model used these control parameters to regulate the electric loads to save energy (increase energy efficiency) without violating any operational constraints. Having ICE’s energy consumption prediction and optimized control of electric loads capabilities is extremely useful for efficient energy management as they ensure that sufficient energy is generated to meet the demands of the electric loads optimally at any time thereby reducing wasted energy due to excess generation. This, in turn, reduces carbon emissions and generates energy and cost savings. While the ICE was tested in a small case-study environment, it could be scaled to any smart building environment

WHY AND HOW PHOTOVOLTAICS WILL PROVIDE CHEAPEST ELECTRICITY IN THE 21ST CENTURY

With the advent of solar panels and windmills, and our ability to generate and use electrical energy locally without the need for long-range transmission, the world is about to witness transformational changes in energy infrastructure. The use of photovoltaics (PV) as source of direct current (DC) power reduces the cost and improves the reliability of PV system. DC microgrid and nanogrid based on PV and storage can provide sustainable electric power to all human beings in equitable fashion. Bulk volume manufacturing of batteries will lead to cost reduction in a manner similar to the cost reduction experience of PV module manufacturing. Future manufacturing innovations and R & D directions are discussed that can further reduce the cost of PV system. If the current trends of PV growth continue, we expect PV electricity cost with storage to reach $0.02 per kWh in the next 8-10 years

Transformative and Disruptive Role of Local Direct Current Power Networks in Power and Transportation Sectors

The power sector is about to undergo a major disruptive transformation. In this paper, we have discussed the best possible energy solution for addressing the challenges of climate change and eradication of energy poverty. This paper focusses on the decentralized power generation, storage and distribution through photovoltaics and lithium batteries. It encompasses the need for local direct current (DC) power through the factors driving this change. The importance of local DC power in the transportation sector is also established. Finally, we conclude with data bolstering our argument towards the paradigm shift in the power network

TRANSFORMATIVE AND DISRUPTIVE ROLE OF LOCAL DIRECT CURRENT POWER NETWORKS IN POWER AND TRANSPORTATION SECTORS

The power sector is about to undergo a major disruptive transformation. In this paper, we have discussed the best possible energy solution for addressing the challenges of climate change and eradication of energy poverty. This paper focusses on the decentralized power generation, storage and distribution through photovoltaics and lithium batteries. It encompasses the need for local direct current (DC) power through the factors driving this change. The importance of local DC power in the transportation sector is also established. Finally, we conclude with data bolstering our argument towards the paradigm shift in the power network