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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Energy performance augmentation of domestic refrigerators with microchannel condensers

Refrigerators are one of the most extensively used household appliances, accounting for approximately 4% of total household electricity consumption. Enhancing their energy performance can significantly reduce residential energy demand. As a result, this study focuses on reducing the energy consumption of a French-door, bottom-mount refrigerator using isobutane (R600a) as the working fluid. Here, the performance improvement of the refrigerators has been obtained mainly through improving the vapor compression refrigeration cycle, with a particular emphasis on condenser design. This study explores the impact of three-pass serpentine microchannel condenser on energy performance, along with the effects of two distinct fan combinations. Charge optimization experiments were conducted to identify the optimal refrigerant mass for the refrigerator unit with microchannel condenser. Additionally, a comprehensive numerical model was developed to analyze the behavior of vapor compression cycle under various refrigerant charges. The experimental findings revealed that the refrigerator unit with the three-pass microchannel condenser reduces the energy consumption of isobutane-based domestic refrigerators up to 16% while reducing the refrigerant charge by 11% in comparison to the refrigerator unit with conventional wire-and-tube condenser.

Charge optimization↗
DOE OSTI2018

Association rule mining based quantitative analysis approach of household characteristics impacts on residential electricity consumption patterns

The comprehensive understanding of the residential electricity consumption patterns (ECPs) and how they relate to household characteristics can contribute to energy efficiency improvement and electricity consumption reduction in the residential sector. After recognizing the limitations of current studies (i.e. unreasonable typical ECP (TECP) extraction method and the problem of multicollinearity and interpretability for regression and machine learning models), this paper proposes an association rule mining based quantitative analysis approach of household characteristics impact on residential ECPs trying to address them together. First, an adaptive density-based spatial clustering of applications with noise (DBSCAN) algorithm is utilized to create seasonal TECP of each individual customer only for weekdays. K-means is then adopted to group all the TECPs into several clusters. An enhanced Apriori algorithm is proposed to reveal the relationships between TECPs and thirty five factors covering four categories of household characteristics including dwelling characteristics, socio-demographic, appliances and heating and attitudes towards energy. Results of the case study using 3326 records containing smart metering data and survey information in Ireland suggest that socio-demographic and cooking related factors such as employment status, occupants and whether cook by electricity have strong significant associations with TECPs, while attitudes related factors almost have no effect on TECPs. The results also indicate that those households with more than one person are more likely to change ECP across seasons. The proposed approach and the findings of this study can help to support decisions about how to reduce electricity consumption and CO2 emissions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Deep learning–based digital twins for heat pumps

Heat pumps are effective cooling and heating appliances to save energy in buildings. However, traditional heat pump models are challenging to integrate with building demands in a co-simulation environment because of the nonlinear thermodynamics of refrigerants. Developing digital twin representatives for heat pumps capable of faster calculations with good accuracy is desirable. This study aimed to establish a generic deep learning–based digital twin for heat pumps with a large amount of high-fidelity data. Two refrigerants for two different heat pumps were considered: an air source heat pump with refrigerant R-410A, an air source heat pump with refrigerant CO 2 , a water source heat pump with refrigerant R-410A, and a water source heat pump with refrigerant CO 2 . Furthermore, results showed that the deep learning (long short-term memory) models effectively represented these four heat pumps as a digital twin: (a) accuracy for training and testing showed smaller than 0.02 for heating electricity and heating demands, and (b) the digital twins showed good consistency with original data for heating electricity and heating demands (root mean square errors of less than 0.12 W and 0.19 W, respectively). Therefore, deep learning–based heat pump models can be used in the co-simulation of building mechanical systems.

Air source heat pump↗
DOE OSTI2014

Evaluating sub-national building-energy efficiency policy options under uncertainty: Efficient sensitivity testing of alternative climate, technolgical, and socioeconomic futures in a regional intergrated-assessment model.

Improving the energy efficiency of the building stock, commercial equipment and household appliances can have a major impact on energy use, carbon emissions, and building services. Subnational regions such as U.S. states wish to increase their energy efficiency, reduce carbon emissions or adapt to climate change. Evaluating subnational policies to reduce energy use and emissions is difficult because of the uncertainties in socioeconomic factors, technology performance and cost, and energy and climate policies. Climate change may undercut such policies. Assessing these uncertainties can be a significant modeling and computation burden. As part of this uncertainty assessment, this paper demonstrates how a decision-focused sensitivity analysis strategy using fractional factorial methods can be applied to reveal the important drivers for detailed uncertainty analysis.

climate change↗
DOE OSTI2015

Scalable tuning of building models to hourly data

Energy models of existing buildings are unreliable unless calibrated so they correlate well with actual energy usage. Manual tuning requires a skilled professional, is prohibitively expensive for small projects, imperfect, non-repeatable, non-transferable, and not scalable to the dozens of sensor channels that smart meters, smart appliances, and cheap/ubiquitous sensors are beginning to make available today. A scalable, automated methodology is needed to quickly and intelligently calibrate building energy models to all available data, increase the usefulness of those models, and facilitate speed-and-scale penetration of simulation-based capabilities into the marketplace for actualized energy savings. The "Autotune'' project is a novel, model-agnostic methodology which leverages supercomputing, large simulation ensembles, and big data mining with multiple machine learning algorithms to allow automatic calibration of simulations that match measured experimental data in a way that is deployable on commodity hardware. This paper shares several methodologies employed to reduce the combinatorial complexity to a computationally tractable search problem for hundreds of input parameters. Furthermore, accuracy metrics are provided which quantify model error to measured data for either monthly or hourly electrical usage from a highly-instrumented, emulated-occupancy research home.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

An efficient correlation for heat and mass transfer effectiveness in tumble-type clothes dryer drums

A heat and mass transfer effectiveness definition relevant to clothes dryers is developed in this work. A correlation is presented for determining the effectiveness of heat and mass transfer in a horizontal-axis, tumble-type clothes dryer drum with axial airflow. The correlation is a function of four measurable quantities: air mass flow rate, cloth mass, air mass in the drum, and the fall time experienced by a cloth falling the full height (i.e. diameter) of the drum. The Buckingham Pi Theorem is applied to the problem in order to derive dimensionless terms upon which the effectiveness depends. Empirical data from several dryers are used to derive an empirical correlation as a function of the dimensionless variables. Three cloth types were investigated, and a separate empirical correlation is proposed for each. Accordingly, the drum effectiveness concept and the correlation presented provide a new, more accurate, computationally efficient, and readily implemented framework for modeling and simulating clothes dryers. It is relevant to conventional gas and electric clothes drying appliances, vapor compression heat pump dryers, and thermoelectric heat pump clothes dryers. Along with appropriate empirical inputs, the framework is extensible to any thermal-evaporative cloth drying systems, including radial-flow horizontal axis tumble drying drums.

42 ENGINEERING↗
DOE OSTI2021

The flexibility gap: Socioeconomic and geographical factors driving residential flexibility

Residential consumers are moving to the center of electricity systems and their flexibility is seen as a key resource to integrate renewable energy sources and support the grid. However, residential flexibility capacities are not homogeneous, as they depend on household appliances, comfort patterns, occupancy, and climate conditions. In this work, we calculate the technical flexibility capacities of 45 consumer types in mainland Spain, organised according to income and regional criteria. We show that flexibility gaps exist at both regional and socioeconomic (income) levels with flexibility differences of up to 10 times more capacity between the household groups from the lowest to the highest capacities. These geographical and socioeconomic gaps in flexibility can lead to distortions in national markets and have the potential to exclude citizens from the provision of flexibility services. Our results show in quantitative terms that a consumer-centered approach without considering correcting measures nor these gaps in drafting energy policies may lead to increasing inequality levels in the residential sector. Under an economic competitive paradigm, households with lower income levels or located in regions with lower flexibility potential may be excluded from the provision of flexibility to the detriment of households with larger potential, raising justice concerns in a flexibility-based energy transition.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Urinary polycyclic aromatic hydrocarbon metabolites among 3-year-old children from Krakow, Poland

Highlights: • ETS, combustion of gas, coal or wood indoors were associated with OH-PAH levels. • The predominant metabolites in urine were OH-napthalenes and OH-fluorenes. • In biomonitoring of PAHs exposure, measuring only 1-OH-pyrene is insufficient. • Urinary OH-PAHs only modestly reflect high molecular weight PAHs exposures. Polycyclic aromatic hydrocarbons (PAHs) are widespread in the environment and can adversely affect human health. The aim of the present study is to describe the level of PAHs exposure in children living in Kraków, one of the most polluted cities in Poland, and to determine the relationship of urinary biomarkers with environmental PAHsexposure. Urinary monohydroxy metabolites (OH-PAHs) of 20 PAHs were assessed in 218 three-year old children, of which only 10 were present in nearly all the samples: monohydroxy metabolites of naphthalene, fluorene, phenantrene and pyrene. Of the metabolites analyzed, hydroxynaphthalenes were predominant and constituted almost 73% of total excreted OH-PAHs, while 1-OH-PYRene was the least abundant (2.3% of total OH-PAHs). All measured urinary OH-PAHs were statistically significantly correlated with each other (R = 0.165–0.880) but the highest correlation coefficients with other individual OH-PAHs and with total OH-PAHs were observed for 2-OH-FLUOR. Children exposed at home to environmental tobacco smoke (ETS) had higher concentrations of fluorene and pyrene urinary metabolites compared to those without ETS exposure; and those exposed to gas-based appliances used for cooking or heating water had higher levels of fluorene and phenanthrene metabolites than children not exposed. The use of coal, wood or oil for heating was associated with elevated levels of 1-OH-PYRene. Urinary PAHs metabolites only modestly reflect high molecular weight carcinogenic PAHs exposures such as those monitored in air in the present study. None of the measured PAHs metabolites was correlated with airborne PM{sub 2.5} and only two were slightly correlated with measured higher molecular mass airborne PAHs. The average concentrations of these specific metabolites in Polish children were much higher than observed in other pediatric populations living in developed countries. Our findings suggest that to capture various sources of PAHs, in addition to 1-OH-PYRene, biomonitoring of PAHs exposure should include 2-OH-NAP and 2-OH-FLUOR.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Concentrations of organophosphate flame retardants and plasticizers in urine from young children in Queensland, Australia and associations with environmental and behavioural factors

Highlights: • Eight OPFRs and eleven metabolites were measured in the urine of Australian kids. • Age was positively associated with BBOEP and negatively associated with BCIPP. • Mouthing activities were associated with higher concentrations of TBOEP, and TPHP. • Children living with window AC units had higher concentrations of BCIPHIPP and DBP. In recent years, the production and usage volumes of organophosphate flame retardants (OPFRs) has increased substantially. Certain OPFRs are suspected reproductive toxins, carcinogenic, and neurotoxic. Insufficient information is available on human exposure pathways to these chemicals, particularly in Australia. We aim to assess the association between OPFR concentrations in the urine of children to environmental and behavioural risk factors. Concentrations of eight OPFRs and eleven metabolites were measured in the urine of 51 children, aged 3–29 months, in Southeast Queensland, Australia and compared to their behavioural and environmental risk factor data obtained by an online questionnaire. Of the 11 OPFR metabolites analysed, 55% were frequently detected in the majority (> 80%) of samples. The most frequently detected metabolite was bis(1,3-dichloroisopropyl) phosphate (BDCIPP) (detected in 100% of samples), followed by 1-hydroxy-2-propyl bis(1-chloro-2-propyl) phosphate (BCIPHIPP) (96%), diphenyl phosphate (DPHP) (94%) and bis(1-chloroisopropyl) phosphate (BCIPP) (86%). In multivariable modelling, age was positively associated with concentrations of bis(2-butoxyethyl) phosphate (BBOEP) and negatively associated with concentrations of BCIPP and BCIPHIPP. Other non-age related factors, including vacuuming frequency, hand-washing frequency and presence and number of some electrical appliances in the home were also associated with concentrations of OPFR metabolites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Why is it still too warm or cold in my house? Examining the relationships between energy efficient capital and household energy insecurity

Here, this paper examines the relationships between energy efficient (EE) capital technology and household energy insecurity in the United States. The theoretical model of these relationships employs household production theory to capture the demand for and production of household energy services, and a stochastic production frontier approach to describe how having access to and the usage intensity of EE capital technology could help alleviate inefficiency in the production of household energy services. A working hypothesis formulated from the theoretical model posits that having EE capital technology in the home will reduce the level of household energy insecurity experienced. The extent of energy insecurity experienced is inferred from an energy insecurity index value assigned to each household, generated via the application of a dichotomous Rasch model to questions contained in the 2015 Residential Energy Consumption Survey. Noting the potential simultaneous relationship that exists between a household having access to and the usage intensity of EE capital technology and the experience of being energy insecure, an instrumental variables approach was employed to estimate a series of ordered logit models. Results suggest access to EE capital technology in the form of Energy Star® appliances, Energy Star® windows, or a SMART thermostat does not reduce the probability of experiencing a greater level of energy insecurity. Nor does the usage intensity of EE capital. Thus, policy instruments designed to alleviate household energy insecurity may need to go beyond simply helping households obtain EE capital technology.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Enabling Graph Mining in RDF Triplestores using SPARQL for Holistic In-situ Graph Analysis

The graph analysis is now considered as a promising technique to discover useful knowledge in data with a new perspective. We envi- sion that there are two dimensions of graph analysis: OnLine Graph Analytic Processing (OLGAP) and Graph Mining (GM) where each respectively focuses on subgraph pattern matching and automatic knowledge discovery in graph. Moreover, as these two dimensions aim to complementarily solve complex problems, holistic in-situ graph analysis which covers both OLGAP and GM in a single system is critical for minimizing the burdens of operating multiple graph systems and transferring intermediate result-sets between those systems. Nevertheless, most existing graph analysis systems are only capable of one dimension of graph analysis. In this work, we take an approach to enabling GM capabilities (e.g., PageRank, connected-component analysis, node eccentricity, etc.) in RDF triplestores, which are originally developed to store RDF datasets and provide OLGAP capability. More specifically, to achieve our goal, we implemented six representative graph mining algorithms using SPARQL. The approach allows a wide range of available RDF data sets directly applicable for holistic graph analysis within a system. For validation of our approach, we evaluate performance of our implementations with nine real-world datasets and three different computing environments - a laptop computer, an Amazon EC2 instance, and a shared-memory Cray XMT2 URIKA-GD graph-processing appliance. The experimen- tal results show that our implementation can provide promising and scalable performance for real world graph analysis in all tested environments. The developed software is publicly available in an open-source project that we initiated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Algorithms and data structures to accelerate network analysis

As the sheer amount of computer generated data continues to grow exponentially, new bottlenecks are unveiled that require rethinking our traditional software and hardware architectures. Here in this paper we present five algorithms and data structures (long queue emulation, lockless bimodal queues, tail early dropping, LFN tables, and multiresolution priority queues) designed to optimize the process of analyzing network traffic. We integrated these optimizations on R-Scope, a high performance network appliance that runs the Bro network analyzer, and present benchmarks showcasing performance speed ups of 5X at traffic rates of 10 Gbps.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Mercury-Impacted Scrap Metal: Source and Nature of the Mercury

The reuse and recycling of industrial solid wastes such as scrap metal is supported and encouraged both internationally and domestically, especially when such wastes can be used as substitutes for raw material. However, scrap metal processing facilities, such as mini-mills, have been identified as a source of mercury (Hg) emissions in the United States. This research aims to better define some of the key issues related to the source and nature of mercury in the scrap metal waste stream. Overall, it is difficult to pinpoint the key mercury sources feeding into scrap metal recycling facilities, quantify their associated mercury concentrations, or determine which chemical forms are most significant. Potential sources of mercury in scrap metal include mercury switches from discarded vehicles, electronic-based scrap from household appliances and related industrial systems, and Hg-impacted scrap metal from the oil and gas industry. The form of mercury associated with scrap metal varies and depends on the source type. The specific amount of mercury that can be adsorbed and retained by steel appears to be a function of both metallurgical and environmental factors. In general, the longer the steel is in contact with a fluid or condensate that contains measurable concentrations of elemental mercury, the greater the potential for mercury accumulation in that steel. Most mercury compounds are thermally unstable at elevated temperatures (i.e., above 350°C). As such, the mercury associated with impacted scrap is expected to be volatilized out of the metal when it is heated during processing (e.g., shredding or torch cutting) or melted in a furnace. This release of fugitive gas (Hg vapor) and particulates, as well as Hg-impacted bag-house dust and control filters, could potentially pose an occupational exposure risk to workers at a scrap metal processing facility. Thus, identifying and characterizing the key sources of Hg-impacted scrap, and understanding the nature and extent of associated releases, represent a practical research need that is essential for improving the environmental management of Hg-impacted scrap and assessing measures to protect workers from potential health and safety hazards that might be posed by mercury and Hg-impacted scrap.

mercury↗
DOE OSTI2026

Characterizing electrical panel capacity, breaker space, and loads in U.S. single-family homes

Electrical panels distribute electricity to appliances, equipment, and other building loads and maintain safety by preventing overloads of branch circuits, feeders, and utility service wires connecting homes to the grid. Load calculations in the National Electrical Code (NEC) determine the capacity of installed electrical panels in the United States, which are rated in amps (A). Circuit breakers within electrical panels affect the magnitude and number of loads that panels can serve. In home retrofits and renovations, existing electrical panels may not accommodate new electrical loads, either due to insufficient electrical capacity or a lack of available breaker spaces. Homes with these panel constraints may need to replace existing panels with new panels that have greater capacity and breaker space.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Single and multi-layered core-shell structures based on ZnO nanorods obtained by aerosol assisted chemical vapor deposition

Core–shell nanorod structures were prepared by a sequential synthesis using an aerosol assisted chemical vapor deposition technique. Several samples consisting of ZnO nanorods were initially grown over TiO{sub 2} film-coated borosilicate glass substrates, following the synthesis conditions reported elsewhere. Later on, a uniform layer consisting of individual Al, Ni, Ti or Fe oxides was grown onto ZnO nanorod samples forming the so-called single MO{sub x}/ZnO nanorod core–shell structures, where MO{sub x} was the metal oxide shell. Additionally, a three-layer core–shell sample was developed by growing Fe, Ti and Fe oxides alternately, onto the ZnO nanorods. The microstructure of the core–shell materials was characterized by grazing incidence X-ray diffraction, scanning and transmission electron microscopy. Energy dispersive X-ray spectroscopy was employed to corroborate the formation of different metal oxides. X-ray diffraction outcomes for single core–shell structures showed solely the presence of ZnO as wurtzite and TiO{sub 2} as anatase. For the multi-layered shell sample, the existence of Fe{sub 2}O{sub 3} as hematite was also detected. Morphological observations suggested the existence of an outer material grown onto the nanorods and further microstructural analysis by HR-STEM confirmed the development of core–shell structures in all cases. These studies also showed that the individual Al, Fe, Ni and Ti oxide layers are amorphous; an observation that matched with X-ray diffraction analysis where no apparent extra oxides were detected. For the multi-layered sample, the development of a shell consisting of three different oxide layers onto the nanorods was found. Overall results showed that no alteration in the primary ZnO core was produced during the growth of the shells, indicating that the deposition technique used herein was and it is suitable for the synthesis of homogeneous and complex nanomaterials high in quality and purity. In addition, materials absorptance determined from the total transmittance and reflectance spectra revealed a broader absorption interval including visible light, indicating potential uses of these nanostructures on solar energy appliances. - Graphical abstract: Display Omitted - Highlights: • Uniform ZnO nanorods (core)–metal oxide (shell) were obtained sequentially by AACVD. • Shells were structured of homogeneous single or multi-layered non-mixed metal oxides. • ZnO nanorod core was preserved during the shell synthesis. • Optical absorptance revealed visible interval absorption for FeO{sub x} shell samples. • Materials can be suitable for photocatalytic or photovoltaic applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2018

An application of the Analytic Hierarchy Process for prioritizing user preferences in the design of a Home Energy Management System

Most demand response programs focus primarily on commercial and industrial loads. The sheer number of decision-influencing variables combined with a lack of a comprehensive understanding of human behavior make it difficult to develop response programs that encourage greater residential consumer participation. This paper presents a multi-criteria decision making approach using actual survey data for identifying user preferences. The novelty of the work is in considering functionality, cost, and carbon emissions for a given set of home appliances toward energy management. The paper explains the design methodologies used to prioritize the preferences from 1023 survey participants. Furthermore, the results are expected to inform the design of Home Energy Management Systems (HEMS) for participating in demand response programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Localized Actual Meteorological Year File Creator (LAF): A tool for using locally observed weather data in building energy simulations

The Localized Actual Meteorological Year File Creator (LAF) application provides web-based access to real meteorological data and processes it into a weather file suitable for building energy modeling. Building energy consumption is affected by what is inside the building (such as occupants, appliances, HVAC systems, etc.) and by what is outside: the weather conditions the building is exposed to. However, freely available weather data files are limited to a number of specific locations, usually airports, which are often located away from city centers where buildings are concentrated. The authors have developed a new tool that supports the investigation and quantification of micro-climate conditions on building energy consumption. LAF is built on the Python open-source programming language and has a Graphical User Interface (GUI) that allows users to create custom weather data files for building energy simulations. Many sets of actual meteorological year weather data for long time periods are publicly available online (such as the MesoWest database) for thousands of locations in the US. LAF selects weather data according to user specifications and automatically processes it through an API across multiple weather stations and multiple time periods. The user may easily select a specific location, time frame, and time step that best meets their needs. This article presents a useful tool for energy modelers, building designers and operators to assist with building performance analysis and optimization.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2012

Role of the Nrf2-heme oxygenase-1 pathway in silver nanoparticle-mediated cytotoxicity

Silver nanoparticles (nano-Ag) have been widely used in various commercial products including textiles, electronic appliances and biomedical products. However, there remains insufficient information on the potential risk of nano-Ag to human health and environment. In the current study, we have investigated the role of NF-E2-related factor 2 (Nrf2) transcription factor in nano-Ag-induced cytotoxicity. When Nrf2 expression was blocked using interring RNA expression in ovarian carcinoma cell line, nano-Ag treatment showed a substantial decrease in cell viability with concomitant increases in apoptosis and DNA damage compared to the control cells. Target gene analysis revealed that the expression of heme oxygenase-1 (HO-1) was highly elevated by nano-Ag in nonspecific shRNA expressing cells, while Nrf2 knockdown cells (NRF2i) did not increase HO-1 expression. The role of HO-1 in cytoprotection against nano-Ag was reinforced by results using pharmacological inducer of HO-1: cobalt protoporphyrin-mediated HO-1 activation in the NRF2i cells prevented nano-Ag-mediated cell death. Similarly, pharmacological or genetic inhibition of HO-1 in nonspecific control cells exacerbated nano-Ag toxicity. As the upstream signaling mechanism, nano-Ag required the phosphoinositide 3-kinase (PI3K) and p38MAPK signaling cascades for HO-1 induction. The treatment with either PI3K inhibitor or p38MAPK inhibitor suppressed HO-1 induction and intensified nano-Ag-induced cell death. Taken together, these results suggest that Nrf2-dependent HO-1 up-regulation plays a protective role in nano-Ag-induced DNA damage and consequent cell death. In addition, nano-Ag-mediated HO-1 induction is associated with the PI3K and p38MAPK signaling pathways. -- Highlights: ► Role of Nrf2 signaling in silver nanoparticle toxicity. ► Silver nanoparticle toxicity is increased by Nrf2 blockade. ► Nrf2-dependent HO-1 induction protects cells from silver nanoparticle toxicity. ► PI3K and p38MAPK cascades are involved in Nrf2/HO-1 induction.

60 APPLIED LIFE SCIENCES↗