Search NASA⌕ Search

SEARCH · Search NASA

Results for “pattern detection”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Raman signal enhancement by multiple beam excitation and its application for the detection of chemicals

In a typical Raman based sensor, a single laser beam is used for exciting the sample and the backscattered or forward scattered light is collected using collection optics and is analyzed by a spectrometer. We have investigated that by means of exciting the sample with multiple beams, i.e., by dividing the same input power of the single beam into two or three or more beams and exciting the sample from different angles, the Raman signal enhances significantly. Due to the presence of multiple beams passing through the same volume of the sample, an interference pattern is formed and the volume of interaction of excitation beams with the sample increases. By means of this geometry, the enhancement in the Raman signal is observed and it was found that the signal strength increases linearly with the increase in number of excitation beams. Experimental results of this scheme for excitation of the samples are reported for explosive detection at a standoff distance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

The hysteresis response of soil CO 2 concentration and soil respiration to soil temperature

Diurnal hysteresis between soil temperature (T s ) and both CO 2 concentration ([CO 2 ]) and soil respiration rate (R s ) were reported across different field experiments. However, the causes of these hysteresis patterns remain a subject of debate, with biotic and abiotic factors both invoked as explanations. Here, to address these issues, a CO 2 gas transport model is developed by combining a layer-wise mass conservation equation for subsurface gas phase CO 2 , Fickian diffusion for gas transfer, and a CO 2 source term that depends on soil temperature, moisture, and photosynthetic rate. Using this model, a hierarchy of numerical experiments were employed to disentangle the causes of the hysteretic [CO 2 ]-T s and CO 2 flux T s (i.e., F-T s ) relations. Model results show that gas transport alone can introduce both [CO 2 ]-T s and F-T s hystereses and also confirm prior findings that heat flow in soils lead to [CO 2 ] and F being out of phase with T s , thereby providing another reason for the occurrence of both hystereses. The area (A hys ) of the [CO 2 ]-T s hysteresis near the surface increases, while the A hys of the Rs-Ts hysteresis decreases as soils become wetter. Moreover, a time-lagged carbon input from photosynthesis deformed the [CO 2 ]-T s and R s -T s patterns, causing a change in the loop direction from counterclockwise to clockwise with decreasing time lag. An asymmetric 8-shaped pattern emerged as the transition state between the two loop directions. Lastly, tracing the pattern and direction of the hysteretic [CO 2 ]-T s and R s -T s relations can provide new ways to fingerprint the effects of photosynthesis stimulation on soil microbial activity and detect time lags between rhizospheric respiration and photosynthesis.

58 GEOSCIENCES↗
DOE OSTI2024

Spherical time-encoded radiation imaging simulations

Radiation source localization is important for nuclear nonproliferation and can be obtained using time-encoded imaging systems with unsegmented detectors. A scintillation crystal can be used with a moving coded-aperture mask to vary the detected count rate produced from radiation sources in the far field. The modulation of observed counts over time can be used to reconstruct an image with the known coded-aperture mask pattern. Current time-encoded imaging systems incorporate cylindrical coded-aperture masks and have limits to their fully coded imaging field-of-view. This work focuses on expanding the field-of-view to 4π by using a novel spherical coded-aperture mask. A regular icosahedron is used to approximate a spherical mask. This icosahedron consists of 20 equilateral triangles; the faces of which are each subdivided into four equilateral triangle-shaped voxels which are then projected onto a spherical surface, creating an 80-voxel coded-aperture mask. Furthermore, these polygonal voxels can be made from high-Z materials for gamma-ray modulation and/or low-Z materials for neutron modulation. In this work, we present Monte Carlo N-Particle (MCNP) simulations and simple models programmed in Mathematica to explore image reconstruction capabilities of this 80-voxel coded-aperture mask.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Jack-of-all-trades effects drive biodiversity–ecosystem multifunctionality relationships in European forests

There is considerable evidence that biodiversity promotes multiple ecosystem functions (multifunctionality), thus ensuring the delivery of ecosystem services important for human well-being. However, the mechanisms underlying this relationship are poorly understood, especially in natural ecosystems. We develop a novel approach to partition biodiversity effects on multifunctionality into three mechanisms and apply this to European forest data. We show that throughout Europe, tree diversity is positively related with multifunctionality when moderate levels of functioning are required, but negatively when very high function levels are desired. For two well-known mechanisms, ‘complementarity’ and ‘selection’, we detect only minor effects on multifunctionality. Instead a third, so far overlooked mechanism, the ‘jack-of-all-trades’ effect, caused by the averaging of individual species effects on function, drives observed patterns. Simulations demonstrate that jack-of-all-trades effects occur whenever species effects on different functions are not perfectly correlated, meaning they may contribute to diversity–multifunctionality relationships in many of the world’s ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Investigation of multiferroic behavior on flakes-like BiFeO{sub 3}

In present work, multiferroic BiFeO{sub 3} was synthesized by hydrothermal route. The rhombohedral structure was confirmed X-ray diffraction pattern and data fitted with Reitveld refinement using Full-Prof software suite. SEM micrograph shows flake like morphology. Frequency and temperature dependence of dielectric constant and dielectric loss were studied and detected enhancement in dielectric constant. The magnetic measurement indicates antiferromagnetic nature of BFO. P-E curve shows ferroelectic hysteresis loop with remanent polarization (2Pr) 0.3518 µC/cm{sup 2}. The dielectric anomaly observed near T{sub N} can be assigned to magnetoelectric coupling which is useful in device application.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Proton beam position measurement in air using a BPM

A Beam Position Monitor (BPM) is potentially useful to measure the position and phase of the beam in air in a non-destructive way. An air-gap BPM in experiments, such as beam-induced radioactive waste management and dynamic radiography applications, where a so-called air gap is needed, can be utilized to measure the beam position and phase. In this study, a stripline BPM was used in the air-gap of an 800 MeV proton beam transport line. The downstream end of the primary beamline exit window was made of a thin aluminum plate and allowed the beam to travel 1.2 m in ambient air before re-entering into a vacuum drift section. Such a configuration was arranged to examine the BPM effectiveness in atmospheric temperature and pressure where ionization of air occurs. In this study, a high energy (800 MeV), high current (0.6 A beam peak current/pulse) proton beam of 5 mm radius was transported in the air. The beam position relative to the axis was measured by detecting the signature of the beam in a nanosecond scale. This nanosecond scale detection ability was useful to identify other signals such as plasma effects. The BPM signals were processed at a frequency of 201 MHz; thus, one gets a stronger response in a stripline pattern as it was used in this study instead of a dot-type BPM. Experimental data show that the BPM works well in air, but ionization of air or plasma formation could not be measured over the BPM signal. The design, construction, and performance of a BPM in air environment are presented.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Relics of interspecific hybridization retained in the genome of a drought-adapted peanut cultivar

Peanut (Arachis hypogaea L.) is a globally important oil and food crop frequently grown in arid, semi-arid, or dryland environments. Improving drought tolerance is a key goal for peanut crop improvement efforts. Here, we present the genome assembly and gene model annotation for “Line8,” a peanut genotype bred from drought-tolerant cultivars. Our assembly and annotation are the most contiguous and complete peanut genome resources currently available. The high contiguity of the Line8 assembly allowed us to explore structural variation both between peanut genotypes and subgenomes. We detect several large inversions between Line8 and other peanut genome assemblies, and there is a trend for the inversions between more genetically diverged genotypes to have higher gene content. We also relate patterns of subgenome exchange to structural variation between Line8 homeologous chromosomes. Unexpectedly, we discover that Line8 harbors an introgression from A.cardenasii, a diploid peanut relative and important donor of disease resistance alleles to peanut breeding populations. The fully resolved sequences of both haplotypes in this introgression provide the first in situ characterization of A.cardenasii candidate alleles that can be leveraged for future targeted improvement efforts. The completeness of our genome will support peanut biotechnology and broader research into the evolution of hybridization and polyploidy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Comprehensive analyses of 723 transcriptomes enhance genetic and biological interpretations for complex traits in cattle

By uniformly analyzing 723 RNA-seq data from 91 tissues and cell types, we built a comprehensive gene atlas and studied tissue specificity of genes in cattle. We demonstrated that tissue-specific genes significantly reflected the tissue-relevant biology, showing distinct promoter methylation and evolution patterns (e.g., brain-specific genes evolve slowest, whereas testisspecific genes evolve fastest). Through integrative analyses of those tissue-specific genes with large-scale genome-wide association studies, we detected relevant tissues/cell types and candidate genes for 45 economically important traits in cattle, including blood/immune system (e.g., CCDC88C) for male fertility, brain (e.g., TRIM46 and RAB6A) for milk production, and multiple growth-related tissues (e.g., FGF6 and CCND2) for body conformation. We validated these findings by using epigenomic data across major somatic tissues and sperm. Collectively, our findings provided novel insights into the genetic and biological mechanisms underlying complex traits in cattle, and our transcriptome atlas can serve as a primary source for biological interpretation, functional validation, studies of adaptive evolution, and genomic improvement in livestock.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Moving lens effect: Simulations, forecasts, and foreground mitigation

The peculiar motion of massive objects across the line of sight imprints a dipolar temperature anisotropy pattern on the cosmic microwave background known as the moving lens effect. This effect provides a unique probe of the transverse components of the peculiar velocity field, but has not yet been detected due to its small size. We implement and validate a stacking estimator for the moving lens signal using a galaxy catalog as a tracer of massive haloes combined with reconstructed velocities from the galaxy number density field. Using simulations, we forecast detection prospects for the moving lens signal from current and upcoming microwave background and galaxy surveys. Here, we demonstrate a new foreground mitigation strategy likely sufficient for current datasets, and discuss various sources of systematic error and noise. Upcoming galaxy surveys will provide high-significance statistical detections of the moving lens effect.

Astrophysical & cosmological simulations↗
DOE OSTI2026

Characterizing skyrmion flow phases with principal component analysis

Principal component analysis (PCA) is a powerful method that can identify patterns in large, complex data sets by constructing low-dimensional order parameters from higher-dimensional feature vectors. There are increasing efforts to use space-and-time-dependent PCA to detect transitions in nonequilibrium systems that are difficult to characterize with equilibrium methods. Here, we demonstrate that feature vectors incorporating the position and velocity information of driven skyrmions moving through random disorder permit PCA to resolve different types of disordered skyrmion motion as a function of driving force and the ratio of the Magnus force to the dissipation. Since the Magnus force creates gyroscopic motion and a finite Hall angle, skyrmions can exhibit a greater range of flow phases than what is observed in overdamped driven systems with quenched disorder. We show that in addition to identifying previously known skyrmion flow phases, PCA detects several additional phases, including different types of channel flow, moving fluids, and partially ordered states. Guided by the PCA analysis, we further characterize the disordered flow phases to elucidate the different microscopic dynamics and show that the changes in the PCA-derived order parameters can be connected to features in bulk transport measures, including the transverse and longitudinal velocity-force curves, differential conductivity, topological defect density, and changes in the skyrmion Hall angle as a function of drive. We discuss how asymmetric feature vectors can be used to improve the resolution of the PCA analysis, and how this technique can be extended to find disordered phases in other nonequilibrium systems with time-dependent dynamics.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Memory-Related Frontal Brainwaves Predict Transition to Mild Cognitive Impairment in Healthy Older Individuals Five Years Before Diagnosis

Background: Early prognosis of high-risk older adults for amnestic mild cognitive impairment (aMCI), using noninvasive and sensitive neuromarkers, is key for early prevention of Alzheimer’s disease. We have developed individualized measures in electrophysiological brain signals during working memory that distinguish patients with aMCI from age-matched cognitively intact older individuals. Objective: Here we test longitudinally the prognosis of the baseline neuromarkers for aMCI risk. We hypothesized that the older individuals diagnosed with incident aMCI already have aMCI-like brain signatures years before diagnosis. Methods: Electroencephalogram (EEG) and memory performance were recorded during a working memory task at baseline. The individualized baseline neuromarkers, annual cognitive status, and longitudinal changes in memory recall scores up to 10 years were analyzed. Results: Seven of the 19 cognitively normal older adults were diagnosed with incident aMCI for a median 5.2 years later. The seven converters’ frontal brainwaves were statistically identical to those patients with diagnosed aMCI (n = 14) at baseline. Importantly, the converters’ baseline memory-related brainwaves (reduced mean frontal responses to memory targets) were significantly different from those who remained normal. Furthermore, differentiation pattern of left frontal memory-related responses (targets versus nontargets) was associated with an increased risk hazard of aMCI (HR = 1.47, 95% CI 1.03, 2.08). Conclusion: The memory-related neuromarkers detect MCI-like brain signatures about five years before diagnosis. The individualized frontal neuromarkers index increased MCI risk at baseline. These noninvasive neuromarkers during our Bluegrass memory task have great potential to be used repeatedly for individualized prognosis of MCI risk and progression before clinical diagnosis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Resource availability governs polyhydroxyalkanoate (PHA) accumulation and diversity of methanotrophic enrichments from wetlands

Aquatic environments account for half of global CH 4 emissions, with freshwater wetlands being the most significant contributors. These CH 4 fluxes can be partially offset by aerobic CH 4 oxidation driven by methanotrophs. Additionally, some methanotrophs can convert CH 4 into polyhydroxyalkanoate (PHA), an energy storage molecule as well as a promising bioplastic polymer. In this study, we investigate how PHA-accumulating methanotrophic communities enriched from wetlands were shaped by varying resource availability (i.e., C and N concentrations) at a fixed C/N ratio. Cell yields, PHA accumulation, and community composition were evaluated in high (20% CH 4 and 10 mM NH 4 + ) and low resource (0.2% CH 4 and 0.1 mM NH 4 + ) conditions simulating engineered and environmental settings, respectively. High resource availability decreased C-based cell yields, while N-based cell yields remained stable, suggesting nutrient exchange patterns differed between methanotrophic communities at different resource concentrations. PHA accumulation was only observed in high resource enrichments, producing approximately 12.6% ± 2.4% (m/m) PHA, while PHA in low resource enrichments remained below detection. High resource enrichments were dominated by Methylocystis methanotrophs, while low resource enrichments remained significantly more diverse and contained only a minor population of methanotrophs. This study demonstrates that resource concentration shapes PHA-accumulating methanotrophic communities. Together, this provides useful information to leverage such communities in engineering settings as well as to begin understanding their role in the environment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Reassimilation of Leaf Internal CO 2 Contributes to Isoprene Emission in the Neotropical Species Inga edulis Mart.

Isoprene (C 5 H 8 ) is a hydrocarbon gas emitted by many tree species and has been shown to protect photosynthesis under abiotic stress. Under optimal conditions for photosynthesis, ~70%–90% of carbon used for isoprene biosynthesis is produced from recently assimilated atmospheric CO 2 . While the contribution of alternative carbon sources that increase with leaf temperature and other stresses have been demonstrated, uncertainties remain regarding the biochemical source(s) of isoprene carbon. In this study, we investigated leaf isoprene emissions ($Is$) from neotropical species Inga edulis Mart. as a function of light and temperature under ambient (450 µmol m -2 s -1 ) and CO 2 -free (0 µmol m -2 s -1 ) atmosphere. Is under CO 2 -free atmosphere showed light-dependent emission patterns similar to those observed under ambient CO 2 , but with lower light saturation point. Leaves treated with the photosynthesis inhibitor DCMU (3-(3,4-dichlorophenyl)-1,1-dimethylurea) failed to produce detectable Is in normal light under a CO 2 -free atmosphere. While strong temperature-dependent Is were observed under CO 2 -free atmosphere in the light, dark conditions failed to produce detectable Is even at the highest temperatures studied (40 °C). Treatment of leaves with 13 C-labeled sodium bicarbonate under CO 2 -free atmosphere resulted in Is with over 50% containing at least one 13 C atom. Is under CO 2 -free atmosphere and standard conditions of light and leaf temperature represented 19% ± 7% of emissions under ambient CO 2 . The results show that the reassimilation of leaf internal CO 2 contributes to Is in the neotropical species I. edulis. Through the consumption of excess photosynthetic energy, our results support a role of isoprene biosynthesis, together with photorespiration, as a key tolerance mechanism against high temperature and high light in the tropics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Regional patterns of genetic diversity in swine influenza A viruses in the United States from 2010 to 2016

Background Regular spatial and temporal analyses of the genetic diversity and evolutionary patterns of influenza A virus (IAV) in swine inform control efforts and improve animal health. Initiated in 2009, the USDA passively surveils IAV in U.S. swine, with a focus on subtyping clinical respiratory submissions, sequencing the hemagglutinin (HA) and neuraminidase (NA) genes at a minimum, and sharing these data publicly. Objectives In this study, our goal was to quantify and describe regional and national patterns in the genetic diversity and evolution of IAV in U.S. swine from 2010 to 2016. Methods A comprehensive phylogenetic and epidemiological analysis of publicly available HA and NA genes generated by the USDA surveillance system collected from January 2010 to December 2016 was conducted. Results The dominant subtypes and genetic clades detected during the study period were H1N1 (H1-γ/1A.3.3.3, N1-classical, 29%), H1N2 (H1-δ1/1B.2.2, N2-2002, 27%), and H3N2 (H3-IV-A, N2-2002, 15%), but many other minor clades were also maintained. Year-round circulation was observed, with a primary epidemic peak in October-November and a secondary epidemic peak in March-April. Partitioning these data into 5 spatial zones revealed that genetic diversity varied regionally and was not correlated with aggregated national patterns of HA/NA diversity. Conclusions These data suggest that vaccine composition and control efforts should consider IAV diversity within swine production regions in addition to aggregated national patterns.

Infectious Diseases↗
DOE OSTI2018

Thermal detection of single e-h pairs in a biased silicon crystal detector

We demonstrate that individual electron-hole pairs are resolved in a 1 cm 2 by 4 mm thick silicon crystal (0.93 g) operated at ~35 mK. One side of the detector is patterned with two quasiparticle-trap-assisted electro-thermal-feedback transition edge sensor arrays held near ground potential. The other side contains a bias grid with 20% coverage. Bias potentials up to ±160 V were used in the work reported here. A fiber optic provides 650 nm (1.9 eV) photons that each produce an electron-hole (e – h + ) pair in the crystal near the grid. The energy of the drifting charges is measured with a phonon sensor noise σ ~0.09 e – h + pair. In conclusion, the observed charge quantization is nearly identical for h + s or e – s transported across the crystal.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Design, Detection, and Countermeasure of Frequency Spectrum Attack and Its Impact on Long Short-Term Memory Load Forecasting and Microgrid Energy Management

This paper introduces a frequency-domain false data injection attack called Frequency Spectrum Attack (FSA) and explores its effects on load forecasting and the energy management system (EMS) in a microgrid. The FSA analyzes time-series signals in the frequency domain to identify patterns in their frequency spectrum. It learns the distribution of dominant frequencies in a dataset of healthy signals. Subsequently, it manipulates the amplitudes of dominant frequencies within this healthy distribution, ensuring a stealthy attack against statistical analysis of the signal spectrum. We evaluated the performance of FSA on LSTM, a state-of-the-art network for load forecasting. The results show that FSA can triple the Mean Absolute Error (MAE) of predictions compared to the normal case and increase it by 70% compared to noise injection attacks. Furthermore, FSA indirectly enhances battery utilization in the EMS by 45%. We then proposed a detection method that combines statistical analysis and machine-learning-based classification techniques with features. The model effectively distinguishes FSA from healthy and noisy signals, achieving an accuracy of 98.7% and an F1-score of 98.1% on a load dataset, covering healthy, FSA, and noisy load data. Finally, a countermeasure was introduced based on the statistical analysis of the frequency spectrum of healthy signals to mitigate the impact of FSA. This countermeasure successfully reduces the MAE of the attacked model from 0.135 to 0.053, validating its effectiveness in mitigating FSA.

Nazeri, Amirhossein↗
DOE OSTI2014

Development of taste sensor system for differentiation of Indonesian herbal medicines

In Indonesia, herbal medicines are usually produced by small and medium enterprises which are relatively low in quality control. The purpose of this paper is to report that we have developed a taste sensor system with global selectivity, i.e., electronic tongue (e-tongue) for differentiation of Indonesian herbal medicines. The e-tongue was composed of five kinds of ion selective electrodes as working electrodes, data acquisition system, and pattern recognition system. Each ion selective electrode (ISE) was built by attaching lipid/polymer membrane. For this purpose, the five kinds of membranes were built by mixing lipid, plasticizer (nitrophenyl octyl ether/NPOE), polyvinyl chloride (PVC), and tetrahydrofuran (THF). In this study, we employed five kinds of lipid, namely oleic acid (OA), dioctyl phosphate (DOP), decyl alcohol (DA), dodecylamine (DDC), and trioctyl methyl ammonium chloride (TOMA). In this case, the membranes transform information of taste substances into electric signal. The five kinds of Indonesian herbal medicine were purchased from local supermarket in Yogyakarta, i.e., kunyit asam (made from turmeric and tamarind), beras kencur (made from rice and kencur), jahe wangi (made from ginger and fragrance), sirih wangi (made from betel leaf), and temulawak (made from Javanese ginger). Prior to detecting the taste from the Indonesian herbal medicine samples, each ion selective electrode was tested with five basic taste samples, i.e., for saltiness, sweetness, umami, bitterness, and sourness. All ISEs showed global selectivity to all samples. Furthermore, the array of ISEs showed specific response pattern to each Indonesian herbal medicine. For pattern recognition system, we employed principle component analysis (PCA). As a result, the e-tongue was able to differentiate five kinds of Indonesian herbal medicines, proven by the total variance of first and second principle components is about 93%. For the future, the e-tongue may be developed for quality control application in herbal medicine industries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Post-hazard condition assessment of nuclear piping-equipment systems: Novel approach to feature extraction and deep learning

Over the past decade, the use of artificial intelligence techniques in the field of health-monitoring has gained significant interest, especially for structures such as building and bridges. However, applications to industrial systems such as equipment-piping systems in nuclear plants have not been explored. Here, in this paper, it is shown that the existing techniques developed for buildings and bridges cannot be extended directly to equipment-piping systems as the response of such systems is governed by multiple localized modes unlike that in buildings and bridges. This paper proposes a new approach that consists of three key aspects: (i) a novel vector of degradation-sensitive features extracted from measured data, (ii) using a deep Artificial Neural Network (ANN) for diagnosis of degradation location and degradation severity, and (iii) consideration of uncertainty in degradation severity when training the ANN. Degradation in piping-equipment systems can occur due to flow-accelerated erosion and corrosion. These locations can potentially exhibit damage such as localized yielding or initiation of cracking due to an external event such as an earthquake. Moreover, such locations can at times go undetected by current inspection techniques. Therefore, a robust framework is needed for detection of degradation after a seismic event. This manuscript proposes a proof-of-concept framework, which utilizes data collected from sensors to generate a deep ANN database for predicting degraded locations and severity in a piping-equipment system. Degradation severity is classified as minor, moderate, and severe. In the suggested methodology, a novel vector of degradation-sensitive features is extracted from the sensor data to train the ANN. A simple piping-equipment system is selected to demonstrate feature extraction as a means to simplify pattern recognition, explore the design and parameters of an ANN, and develop a sensor placement strategy. The effectiveness of the proposed framework is demonstrated on a realistic primary safety system of a two-loop nuclear reactor. It is shown that the proposed post-hazard condition assessment framework is able to detect degraded locations along with the severity levels, including minor degradation, with considerably higher accuracy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗