Scaling Configuration of Energy Harvesting Sensors with Reinforcement Learning

With the advent of the Internet of Things (IoT), an increasing number of energy harvesting methods are being used to supplement or supplant battery based sensors. Energy harvesting sensors need to be configured according to the application, hardware, and environmental conditions to maximize their usefulness. As of today, the configuration of sensors is either manual or heuristics based, requiring valuable domain expertise. Reinforcement learning (RL) is a promising approach to automate configuration and efficiently scale IoT deployments, but it is not yet adopted in practice. We propose solutions to bridge this gap: reduce the training phase of RL so that nodes are operational within a short time after deployment and reduce the computational requirements to scale to large deployments. We focus on configuration of the sampling rate of indoor solar panel based energy harvesting sensors. We created a simulator based on 3 months of data collected from 5 sensor nodes subject to different lighting conditions. Our simulation results show that RL can effectively learn energy availability patterns and configure the sampling rate of the sensor nodes to maximize the sensing data while ensuring that energy storage is not depleted. The nodes can be operational within the first day by using our methods. We show that it is possible to reduce the number of RL policies by using a single policy for nodes that share similar lighting conditions.Comment: 7 pages, 5 figure

Hybrid Cooling Systems for Low-Temperature Geothermal Power Production

The overall objective of this investigation is to identify and evaluate methods by which the net power output of an air-cooled geothermal power plant can be enhanced during hot ambient conditions using minimal amounts of water. Geothermal power plants that use air-cooled heat rejection systems experience a decrease in power production during hot periods of the day. This decrease in power output typically coincides with the time when utilities need power to address high air conditioning loads. Hybrid cooling options, which use both air and water, have been studied for this report to assess how they might mitigate the net power decrease

Watt-level dysprosium fiber laser at 3.15 {\mu}m with 73% slope efficiency

Rare-earth-doped fiber lasers are emerging as promising high-power mid-infrared sources for the 2.6-3.0 {\mu}m and 3.3-3.8 {\mu}m regions based on erbium and holmium ions. The intermediate wavelength range, however, remains vastly underserved, despite prospects for important manufacturing and defense applications. Here, we demonstrate the potential of dysprosium-doped fiber to solve this problem, with a simple in-band pumped grating-stabilized linear cavity generating up to 1.06 W at 3.15 {\mu}m. A slope efficiency of 73% with respect to launched power (77% relative to absorbed power) is achieved: the highest value for any mid-infrared fiber laser to date, to the best of our knowledge. Opportunities for further power and efficiency scaling are also discussed

An instance of mass mortality in the Muttukadu farm near Madras during April 1983

An unusual mortality of fishes and other organisms was observed in the open site surrounding the farm at Mariculture Centre of CMFRI, Muttukadu.at 1983. During this season the prevailing high temperature and salinity and the enclosed nature of the water body, all resulted in an intense bloom of dinophytes. This led to oxygen depletion of the waters and then, very likely, of the mud. The regular operation of drag nets and gill nets has stirred up the mud and created large scale disturbance which, combined with the oxygen depletion, must have precipitated the crisis and led to the mortality of prawns and fishes

Inkjet printed LED based pH chemical sensor for gas sensing

Predictable behaviour is a critical factor when developing a sensor for potential deployment within a wireless sensor network (WSN). The work presented here details the fabrication and performance of an optical chemical sensor for gaseous acetic acid analysis, which was constructed using inkjet printed deposition of a colorimetric chemical sensor. The chemical sensor comprised a pH indicator dye (bromophenol blue), phase transfer salt tetrahexylammonium bromide and polymer ethyl cellulose dissolved in 1-butanol. A paired emitter-detector diode (PEDD) optical detector was employed to monitor responses of the colorimetric chemical sensor as it exhibits good sensitivity, low power consumption, is low cost, accurate and has excellent signal to noise ratios. The chemical sensor formulation was printed directly onto the surface the emitter LED, and the resulting chemical sensors characterised with respect to their layer thickness, response time and recovery time. The fabrication reproducibility of inkjet printed chemical sensors in comparison to drop casted chemical sensors was investigated. Colorimetric chemical sensors produced by inkjet printing, exhibited an improved reproducibility for the detection of gaseous acetic acid with a relative standard deviation of 5.5 % in comparison to 68.0 % calculated for drop casted sensors (n = 10). The stability of the chemical sensor was also investigated through both intra and inter-day studies

Mastering Technology Audit Quality Assurance: A Framework for Auditors

Ramkumar\u27s document presents a comprehensive framework (COBIT, NIST, FFIEC CAT, FFIEC IT Handbook) for establishing, implementing, and maturing a robust Technology Quality Assurance Review (QAR) program within internal audit, emphasizing its strategic imperative in today\u27s complex digital landscape. Driven by escalating technology risks (cybersecurity, cloud, AI), evolving regulatory demands (FFIEC, COBIT, NIST CSF, GLBA, SOX, NYDFS, State Privacy Laws), and the need for reliable assurance, the framework details a systematic approach grounded in IIA Global Internal Audit Standards (specifically the QAIP) and integrated with key governance and control frameworks like COBIT and NIST CSF. It outlines core QAR principles, a phased implementation roadmap, a maturity model for assessing and advancing capabilities, and the critical role of leadership in driving quality. The core of the framework involves a detailed methodology for QAR, often executed via checklists, designed to rigorously evaluate the entire lifecycle of technology audits – from planning, risk assessment, and scoping through fieldwork execution, evidence gathering (sufficiency, reliability, relevance), findings development (including root cause analysis), and reporting quality. The framework stresses the need for QAR to assess audit coverage and technical rigor across crucial domains such as cybersecurity and resilience, IT General Controls (ITGCs), cloud computing assurance, data governance/analytics/AI, third-party risk management, and specific regulatory compliance. An example application is provided through a detailed QAR checklist tailored for IT audits of online banking services. Ultimately, this integrated approach aims to enhance technology audit quality and effectiveness, provide credible assurance to stakeholders, foster continuous improvement within the internal audit function, and demonstrate its value in governing technology risk