Abstract

Particulate matter (PM) is considered the primary contributor to air pollution and has severe implications for general health. PM concentration has high spatial variability and thus needs to be monitored locally. Traditional PM monitoring setups are bulky, expensive and cannot be scaled for dense deployments. This paper argues for a densely deployed network of IoT-enabled PM monitoring devices using low-cost sensors. In this work, 49 devices were deployed in a region of the Indian metropolitan city of Hyderabad out-of this, 43 devices were developed as part of this work and 6 devices were taken off the shelf. The low-cost sensors were calibrated for seasonal variations using a precise reference sensor. A thorough analysis of data collected for seven months has been presented to establish the need for dense deployment of PM monitoring devices. Different analyses such as mean, variance, spatial interpolation and correlation have been employed to generate interesting insights about temporal and seasonal variations of PM. In addition, event-driven spatiotemporal analysis is done for PM values to understand the impact of the bursting of firecrackers on the evening of the Diwali festival. A web-based dashboard is designed for real-time data visualization.

Abstract

Indian residences are vulnerable to heat-driven discomfort amid the mounting prevalence of weather extremes, residential design and construction practices, and densifying urbanscapes. Therefore, it is vital to understand the thermal comfort characteristics of nationwide residences. This study proposes an adaptive thermal comfort model based on yearlong field surveys in eight cities located across five climate zones of India – the India Model for Adaptive Comfort - Residential (IMAC-R). The model prescribes the operative temperature bands for 80% and 90% thermal acceptability in correlation with the outdoor reference temperature, applicable to mixed-mode (MM) and naturally ventilated (NV) residences. More than 80% of the Indian residential occupants experienced a neutral thermal sensation in the indoor operative range of 16.3–35 ◦C in response to a 5.5–33 ◦C variation in the 30-day outdoor running mean temperature. Comparing the proposed model with the PMV model revealed that the latter underpredicts the thermal adaptivity of Indian occupants. The model was also compared against its predecessor – India Model for Adaptive Comfort for Commercial Buildings (IMAC MM and NV), along with relevant global and regional thermal comfort models. On average, the neutral temperature prescribed by IMAC-R was warmer than the temperatures prescribed by IMAC MM and NV by 2.9 ◦C and 2.1 ◦C, respectively; it was also warmer than the temperature prescribed by the recent ASHRAE-55 and EN 16798-1 models by 2 ◦C and 0.3 ◦C, respectively. IMAC-R reserves the prospect of addressing the thermal comfort needs of the national population while paving the way for longterm energy savings and climate action.

Abstract

Air Conditioners (ACs) have become a major contributor to residential electricity consumption in India. Non-intrusive Load Monitoring (NILM) can be used to understand residential AC use and its contribution to electricity consumption. NILM techniques use ground truth information along with meter readings to train disaggregation algorithms. There are datasets available for disaggregation, but no dataset is available for a hot tropical country like India especially for AC event detection. Our dataset’s primary objective is to help train NILM algorithms for AC event detection and compressor operations. The dataset comprises of home-level electrical current consumption and manually tagged AC ground truth (ON/OFF status) data at 1-min interval, indoor environment temperature and relative humidity readings at 5-min interval and dwelling, AC and household characteristics. The data was collected from 11 homes located in a composite climate zone-Hyderabad, India for 19 summer days (May) 2019. The dataset consists of 1.6 million data points and 450 AC cycles with each cycle having a runtime of more than 60 min (>2000 compressor ON/OF cycles). Public availability of such a dataset will allow researchers to develop, train and test NILM algorithms that recognize AC and identify compressor operations. Keywords: Current consumption, NILM, Air Conditioner, Residential electricity, India

Abstract

A tool Smart Glazing Simulator (SGLSim), has been developed to perform parametric simulation analysis of diferent window systems with several window-to-wall ratios and orientations to compute and compare the annual energy performance. The net annual energy performance of the building is based on the electricity consumption in heating, cooling, interior lighting, and appliances, along with the electricity generation by the photovoltaic (PV) glazing, which is used to evaluate the energy performance of smart glazing. Performing parametric energy simulations and calculating the net annual electricity consumption of diferent combinations requires building modeling and energy simulation expertise. A web-based parametric tool can assist the user in carrying out the desired studies without requiring extensive technical knowledge. A case study is prepared for India’s warm and humid climatic zone. This study examines the benefts of double pane semi-transparent photovoltaics (STPV) glazing, STPV glazing with dynamic internal blind, and electrochromic (EC) glazing over other traditional glazing systems. The study shows that the optimal net annual electricity consumption in the case of STPV windows is 10–12% less than the optimal value obtained in a simple glazing case. Additionally, the result suggested that glare-controlled interior blinds in the STPV window further reduce the net annual electricity consumption by up to 15% compared to conventional glazing. Similarly, installing the EC glazing reduces the yearly electricity consumption by up to 5% compared to standard glazing. Keywords: Smart glazing, Energy performance, Parametric simulations

Abstract

The residential sector accounts for around 24% of the total electricity consumption in India. Recent studies show that air conditioners (ACs) have become a signifcant contributor to residential electricity consumption. Further, it is predicted that by 2037, the demand for ACs will increase by four times due to their afordability and availability. Not many studies have been found on residential AC usage patterns and the factors (AC load, setpoint, hours of usage) that infuence household electricity consumption. This paper investigates the residential AC usage patterns and AC’s contribution to total residential electricity consumption. Twenty-fve urban homes from a wet and dry climatic region of India were monitored for nine months (in 2019) to determine overall household electricity consumption patterns, AC usage, and indoor environment during summer, monsoon, and winter. Analysis of seasonal consumption patterns shows a signifcant diference in electricity usage between homes with ACs and homes without ACs during the summer season. The average electricity consumption for AC homes was 15.1 kWh/day during summer, 6.6 kWh/day during monsoon, and 6.1 kWh/day during the winter season. Results showed that AC alone contributed to 39% of the total household consumption in summers. The peak AC usage in all homes is observed during sleep hours which was generally between 10:00 pm and 6:00 am and the average AC runtime was 6.2 h. The average indoor temperature was recorded as 26.9 °C during the AC ON period. The AC peak load, i.e., the maximum electricity demand during the AC ON period, is 1.7 kW on average during the study period. The average annual consumption of homes with ACs was 2881 kWh, and for non-AC homes, the consumption was 2230 kWh. Findings from our analysis provide a detailed understanding of AC consumption profles and the diference in electricity consumption characteristics between AC and non-AC homes across diferent seasons. Keywords: Residential electricity, Household consumption patterns, Household load monitoring, Air conditioning load monitoring, AC homes, Non-AC homes, India

Abstract

When the Indian government declared the frst lockdown on 25 March 2020 to control the increasing number of COVID-19 cases, people were forced to stay and work from home. The aim of this study is to quantify the impact of stay-at-home orders on residential Air Conditioning (AC) energy and household electricity consumption (excluding AC energy). This was done using monitored data from 380 homes in a group of fve buildings in Hyderabad, India. We gathered AC energy and household electricity consumption data at a 30-min interval for each home individually in April 2019 and April 2020. Descriptive and inferential statistical analysis was done on this data. To ofset the diference in temperatures for the month of April in 2019 and 2020, only those weekdays were selected where the average temperature in 2019 was same as the average temperature in 2020. The study establishes that the average number of hours the AC was used per day in each home increased in the range 4.90–7.45% depending on the temperature for the year 2020. Correspondingly, the overall AC consumption increased in the range 3.60–4.5%, however the daytime (8:00 AM to 8:00 PM) AC energy consumption increased in the range 22–26% and nighttime (8:00 PM to 8:00 AM) AC energy consumption decreased by 5–7% in the year 2020. The study showed a rise in household electricity consumption of about 15% for the entire day in the year 2020. The household electricity consumption increased during daytime by 22- 27.50% and 1.90- 6.6% during the nighttime. It was observed that the morning household electricity peak demand shifted from 7:00 AM in 2019 to 9:00 AM in 2020. Conversely, the evening peak demand shifted from 9:00 PM in 2019 to 7:00 PM in 2020. An additional peak was observed during afternoon hours in the lockdown. Keywords: COVID-19, Residential energy consumption, Air conditioning energy consumption, Household electricity consumption, Case study, Data monitoring, India

Abstract

Bacterial cellulase enzymes are potent candidates for the efficient production of bioethanol, a promising alternative to fossil fuels, from cellulosic biomass. These enzymes catalyze the breakdown of cellulose in plant biomass into simple sugars and then to bioethanol. In the absence of the enzyme, the cellulosic biomass is recalcitrant to decomposition due to fermentationresistant lignin and pectin coatings on the cellulose surface, which make them inaccessible for hydrolysis. Cellobiohydrolase CelS is a microbial enzyme that binds to cellulose fiber and efficiently cleaves it into a simple sugar (cellobiose) by a repeated processive chopping mechanism. The two contributing factors to the catalytic reaction rate and the yield of cellobiose are the efficient product expulsion from the product binding site of CelS and the movement of the substrate or cellulose chain into the active site. Despite progress in understanding product expulsion in other cellulases, much remains to be understood about the molecular mechanism of processive action of these enzymes. Here, nonequilibrium molecular dynamics simulations using suitable reaction coordinates are carried out to investigate the energetics and mechanism of the substrate dynamics and product expulsion in CelS. The calculated free energy barrier for the product expulsion is three times lower than that for the processive action indicating that product removal is relatively easier and faster than the sliding of the substrate to the catalytic active site. The water traffic near the active site in response to the product expulsion and the processive action is also explored.

Abstract

In response to the COVID19 pandemic, many countries have implemented lockdowns in multiple phases to ensure social distancing and quarantining of the infected subjects. Subsequent unlocks to reopen the economies started next waves of infection and imposed an extra burden on quarantine to keep the reproduction number (R0) < 1. However, most countries could not efectively contain the infection spread, suggesting identifcation of the potential sources weakening the efect of lockdowns could help design better informed lockdown-unlock cycles in the future. Here, through building quantitative epidemic models and analyzing the metadata of 50 countries from across the continents we frst found that the estimated value of R0, adjusted w.r.t the distribution of medical facilities and virus clades correlates strongly with the testing rates in a country. Since the testing capacity of a country is limited by its medical resources, we investigated if a cost–beneft trade-of can be designed connecting testing rate and extent of unlocking. We present a strategy to optimize this trade-of in a country specifc manner by providing a quantitative estimate of testing and quarantine rates required to allow diferent extents of unlocks while aiming to maintain R0 < 1. We further show that a small fraction of superspreaders can dramatically increase the number of infected individuals even during strict lockdowns by strengthening the positive feedback loop driving infection spread. Harnessing the beneft of optimized country-specifc testing rates would critically require minimizing the movement of these superspreaders via strict social distancing norms, such that the positive feedback driven switch-like exponential spread phase of infection can be avoided/delayed.

Abstract

Tissue homeostasis and regeneration depend on the reversible transitions between quiescence (G0) and proliferation. The liver has a remarkable capacity to regenerate after injury or resection by cell growth and division. During regeneration, the liver needs to maintain the essential metabolic tasks and meet the metabolic requirements for hepatocyte growth and division. Understanding the regulatory mechanisms involved in balancing the liver function and proliferation demand after injury or resection is crucial. In this study, we analyzed high-resolution temporal RNA sequencing data of liver regeneration after two-thirds partial hepatectomy (PHx) using network inference and mathematical modeling approaches. The reconstruction of the dynamic regulatory network of liver regeneration reveals the trajectories of different metabolic pathways, protein processing in the endoplasmic reticulum (ER), ribosome biogenesis, RNA transport, spliceosome, immune response, and cell cycle. We further developed a mathematical model of the integrated circuit of liver regeneration that accountsfor underlying features of compensatory metabolism, proliferation, and epithelial-to-mesenchymal transition during liver regeneration. We show that a mutually exclusive behavior emerges due to the bistable inactivation of HNF4A, which controls the initiation and termination of liver regeneration and different population-level expressions observed in single-cell RNA sequencing data of liver regeneration.

Abstract

Estimating brain age and establishing functional biomarkers that are prescient of cognitive declines resulting from aging and dierent neurological diseases are still open research problems. Functional measures such as functional connectivity are gaining interest as potentially more subtle markers of neurodegeneration. However, brain functions are also aected by “normal” brain aging. More information is needed on how functional connectivity relates to aging, particularly in the absence of neurodegenerative disorders. Resting-state fMRI enables us to investigate functional brain networks and can potentially help us understand the processes of development as well as aging in terms of how functional connectivity (FC) matures during the early years and declines during the late years. We propose models for estimation of the chronological age of a healthy person from the resting state brain activation (rsfMRI). In this work, we utilized a dataset (N = 638, age-range 20–88) comprising rsfMRI images from the Cambridge Centre for Aging and Neuroscience (Cam-CAN) repository of a healthy population. We propose an age prediction pipeline Ayu which consists of data preprocessing, feature selection, and an attention-based model for deep learning architecture for brain age assessment. We extracted features from the static functional connectivity (sFC) to predict the subject’s age and classified them into dierent age groups (young, middle, middle, and old ages). To the best of our knowledge, a classification accuracy of 72.619 % and a mean absolute error of 6.797, and an r 2 of 0.754 reported by our Ayu pipeline establish competitive benchmark results as compared to the state-of-the-art-approach. Furthermore, it is vital to identify how dierent functional regions of the brain are correlated. We also analyzed how functional regions contribute dierently across ages by applying attention-based networks and integrated gradients. We obtained well-known resting-state networks using the attention model, which maps to within the default mode network, visual network, ventral attention network, limbic network, frontoparietal network, and somatosensory network connected to aging. Our analysis of fMRI data in healthy elderly Age groups revealed that dynamic FC tends to slow down and becomes less complex and more random with increasing