This paper presents the design of a Sensor Node for sensing environmental variables in Buildings with the objective to control HVAC (Heating, Ventilation and Air Conditioning) system using a BEMS (Building Energy Management Systems) to achieve energy savings. BEMS is a control system used to manage the energy consumption of a Building trying to achieve energy savings. The designed Sensor Node is capable of measure six different environmental variables: temperature, humidity, and carbon dioxide, using a Sensirion SCD30 sensor; particulate matter 2.5, and 10, using a Sensirion SPS30 sensor, and lighting using a BH1750FVI sensor. The control unit for the Sensor Node is designed using a node MCU ESP8266 microprocessor which is capable to receive the sensors signals. The microprocessor are connected to a XBEE module for communications purpose with a main control system. The main control system is developed in a Raspberry Pi. The objective of this design is control the set point of the HVAC equipment using the values of the sensed environmental variables. The main contribution of this work is to control wirelessly the operation of the HVAC equipment through the set point for a determinate space using the environmental sensor node measures obtained of the space to be controlled.
List of Conference Committee, Steering Committee, Conference General Chairs, Program Chairs, Technical Committee are available in this pdf.
Modern machine learning models consume massive amounts of energy. In a widely cited paper [9], the authors compare the estimated CO2 emissions from training common NLP models like BERT [5], GPT-2 [1], ELMO [8] and transformers [10]. Similarly, in [7], the authors compare other models like Meena [2] and GPT3 [4]. For example, GPT-3 training consumes around 550 metric tons of carbon. Together, data center use including machine learning model creation is projected to grow exponentially in the coming years [6]. As machine learning becomes used by more and more organizations for their business processes, it is imperative new paradigms for efficient model training and inferencing are developed. Our research project is motivated from a recent flight search which showed multiple flight options along with their estimated carbon emissions (Figure 1) . It is left upon the discretion of the traveller to pick a suitable flight based on CO2 emissions, comfort and convenience. We extrapolate the same for training a batch of ML jobs and create a scheduler which can appropriately allocate jobs to different datacenters at different times. In addition, based on the data characteristics and ML task (classification, regression etc.), we can also recommend the appropriate model to be used.
Soil nutrient mapping has been implemented in agriculture for the last 40 years. Ongoing economic pressures in agriculture to increase crop yields while sustaining farmer profitability demands in-depth knowledge of soil nutrients. Spatial and temporal variability of yield-limiting factors have been recognized for a long time, and, with such information, farmers have technologies to manage their fields site-specific. However, farmers still tend to manage their fields uniformly because it takes cost, labor, and money to assess and monitor soil health conditions by traditional lab-based methods. We have used a rapidly emerging hyperspectral sensing technology to assess plant-available nutrient content in soil, especially nitrogen, for the use of optimizing in-season nitrogen management. The new and emerging technology would empower farmers with data to help them make better decisions to grow more profitable crops, protect the environment, while growing more nutritious food
The United States Department of Energy estimates that global energy consumption will double in the next two decades which will lead to extensive utilization of fossil fuels resulting in economic and environmental challenges. Although biofuels are a great alternative, the current generations cannot sustain the demand without threatening food supplies. Third-generation biofuels have lipids that can be converted into biodiesel, jet fuel, and gasoline. Despite these advantages, they are not produced commercially as the lipid yield of the natural strains are roadblocks in their large-scale use.This research aimed to increase TAG production in the microalgae Isochrysis galbana by inoculating it with Bacillus megaterium for more efficient biofuel yield. The study uses a bioreactor created using PVC pipes, 8-port sprinkler, CO2 tank and bottles to grow the microalgae. Secchi stick was used to measure optical density before extracting dried biomass with a french press. After conducting an ANOVA test to compare the samples, the p-value was lesser than 0.01. Later the post-hoc test was run and the groups were found to be significantly different especially for the 4x concentration versus 1x concentration. Results showed that through the co-cultivation of this algae and bacteria, TAG yields are increased. To support the findings of the experiment, bulk RNA sequencing data for Isochrysis galbana and Bacillus megaterium was compared to identify the pathways that were affected in the microalgae. The increase in biofuel productivity is crucial for the bioremediation of wastewater, the bioremoval of CO2 and for improving the economics.
