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DOE OSTI2023

Linking repeat lidar with Landsat products for large scale quantification of fire-induced permafrost thaw settlement in interior Alaska

The permafrost–fire–climate system has been a hotspot in research for decades under a warming climate scenario. Surface vegetation plays a dominant role in protecting permafrost from summer warmth, thus, any alteration of vegetation structure, particularly following severe wildfires, can cause dramatic top–down thaw. A challenge in understanding this is to quantify fire-induced thaw settlement at large scales (>1000 km 2 ). In this study, we explored the potential of using Landsat products for a large-scale estimation of fire-induced thaw settlement across a well-studied area representative of ice-rich lowland permafrost in interior Alaska. Six large fires have affected ~1250 km 2 of the area since 2000. We first identified the linkage of fires, burn severity, and land cover response, and then developed an object-based machine learning ensemble approach to estimate fire-induced thaw settlement by relating airborne repeat lidar data to Landsat products. The model delineated thaw settlement patterns across the six fire scars and explained ~65% of the variance in lidar-detected elevation change. Our results indicate a combined application of airborne repeat lidar and Landsat products is a valuable tool for large scale quantification of fire-induced thaw settlement.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Decay of the proton-unbound superradiant state in 13 N

Here, the 12 C ( 3 He, 𝑑)⁢ 13 N* reaction is studied in an experiment with a high-resolution magnetic spectrograph, in coincidence with protons detected in silicon detectors near the target. This allows for the observation of angular correlation patterns between the proton transfer and proton decays from populated unbound resonances. A formalism describing the spin polarization of direct reactions is developed to analyze these correlations, and is verified on the known directionally asymmetric decay distributions arising from parity mixing in the 13 N ⁡($\frac{3}{2}$ − , $\frac{5}{2}$ + ) doublet. The same formalism is used to study the decays from the continuum-aligned, broad 3/2 + resonance at 7.9 MeV excitation energy, which arises from superradiant coupling. The observed asymmetric angular correlation patterns are approximately reproduced by adding an “artificial” 3/2 − resonance with strength equal to that of the reaction formalism. This parity-mixing approach serves as a first approximation to a more advanced reaction model of rapid reaction and decay sequences.

13N↗

DOE OSTI2019

Topic modeling to discern irregular order patterns in unlabeled electronic health records

This paper presents an application of topic modeling on event sequences of Electronic Health Record (EHR) orders. Metaphorically, we approach clinical order event sequences of unlabeled data as if they are documents where words are the events that occurred in the history of an order. We demonstrate the approach leveraging Consult orders data. The details of the data preprocessing and the data structures are described along with the data sources. Latent Dirichlet Allocation (LDA) is leveraged to fit against the limited datasets prepared. Another open source tool – LDAvis is used for exploratory analysis of the LDA results. The preliminary results revealed some order patterns that are qualitatively evaluated as potential irregular transitions. The goal of this analysis is to provide unsupervised learning application to domain experts in the absence of labeled data where they can investigate captured patterns and identify irregular transitions of orders. Ultimately, such efforts will guide formalization of hazard detection algorithms that monitor EHR data to identify health information technology related hazards.

Ozmen, Ozgur↗

DOE OSTI2020

Kensor: Coordinated Intelligence from Co-Located Sensors

Internet of Things (IoT) is becoming more pervasive in many installations, including homes, manufacturing plants, and industrial facilities of all kinds. The data that IoT produces is a reflection of usual behavior such as daily routines and scheduled tasks, but also from unexpected behavior due to unintentional or undesirable abnormalities. Here, we focus on achieving coordinated intelligence about normal and abnormal phenomena from multiple sensors that are geographically colocated in close proximity, monitoring and controlling a set of co-located devices. Given a set of co-located sensors, we seek an intelligent approach that would automatically determine the “normal” patterns of behaviors among the correlated sensors. After normal behavior is extracted, later monitoring should detect any deviant variations over time. An example application is an entry monitoring and alert system for facilities such as nuclear reactors, where badge readers, door locks, lights, weight trackers and other co-located sensors at the entry point are collectively tracked. To address this problem, we identify the possible solution approach that can be used to solve its different variants. The implemented model is developed as a combination of rules and Markov Chain methods.

Kotevska, Olivera↗

DOE OSTI2016

SU-F-T-91: Development of Real Time Abdominal Compression Force (ACF) Monitoring System

Purpose: Hard-plate based abdominal compression is known to be effective, but no explicit method exists to quantify abdominal compression force (ACF) and maintain the proper ACF through the whole procedure. In addition, even with compression, it is necessary to do 4D CT to manage residual motion but, 4D CT is often not possible due to reduced surrogating sensitivity. In this study, we developed and evaluated a system that both monitors ACF in real time and provides surrogating signal even under compression. The system can also provide visual-biofeedback. Methods: The system developed consists of a compression plate, an ACF monitoring unit and a visual-biofeedback device. The ACF monitoring unit contains a thin air balloon in the size of compression plate and a gas pressure sensor. The unit is attached to the bottom of the plate thus, placed between the plate and the patient when compression is applied, and detects compression pressure. For reliability test, 3 volunteers were directed to take several different breathing patterns and the ACF variation was compared with the respiratory flow and external respiratory signal to assure that the system provides corresponding behavior. In addition, guiding waveform were generated based on free breathing, and then applied for evaluating the effectiveness of visual-biofeedback. Results: We could monitor ACF variation in real time and confirmed that the data was correlated with both respiratory flow data and external respiratory signal. Even under abdominal compression, in addition, it was possible to make the subjects successfully follow the guide patterns using the visual biofeedback system. Conclusion: The developed real time ACF monitoring system was found to be functional as intended and consistent. With the capability of both providing real time surrogating signal under compression and enabling visual-biofeedback, it is considered that the system would improve the quality of respiratory motion management in radiation therapy. This research was supported by the Mid-career Researcher Program through NRF funded by the Ministry of Science, ICT & Future Planning of Korea (NRF-2014R1A2A1A10050270) and by the Radiation Technology R&D program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (No. 2013M2A2A7038291)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2019

Photonic crystal nanobeam biosensors based on porous silicon

Photonic crystal (PhC) nanobeams (NB) patterned on porous silicon (PSi) waveguide substrates are demonstrated for the specific, label-free detection of oligonucleotides. These photonic structures combine the large active sensing area intrinsic to PSi sensors with the high-quality (Q) factor and low-mode volume characteristic of compact resonant silicon-on-insulator (SOI) PhC NB devices. The PSi PhC NB can achieve a Q-factor near 9,000 and has an approximately 40-fold increased active sensing area for molecular attachment, compared to traditional SOI PhC NB sensors. The PSi PhC NB exhibits a resonance shift that is more than one order of magnitude larger than that of a similarly designed SOI PhC NB for the detection of small chemical molecules and 16-base peptide nucleic acids. Furthermore the design and fabrication of PSi PhC NB sensors are compatible with CMOS processing, sensor arrays, and integration with lab-on-chip systems.

36 MATERIALS SCIENCE↗

DOE OSTI2016

THE AGE OF THE YOUNG BULGE-LIKE POPULATION IN THE STELLAR SYSTEM TERZAN 5: LINKING THE GALACTIC BULGE TO THE HIGH- Z UNIVERSE

The Galactic bulge is dominated by an old, metal-rich stellar population. The possible presence and the amount of a young (a few gigayears old) minor component is one of the major issues debated in the literature. Recently, the bulge stellar system Terzan 5 was found to harbor three sub-populations with iron content varying by more than one order of magnitude (from 0.2 up to two times the solar value), with chemical abundance patterns strikingly similar to those observed in bulge field stars. Here we report on the detection of two distinct main-sequence turnoff points in Terzan 5, providing the age of the two main stellar populations: 12 Gyr for the (dominant) sub-solar component and 4.5 Gyr for the component at super-solar metallicity. This discovery classifies Terzan 5 as a site in the Galactic bulge where multiple bursts of star formation occurred, thus suggesting a quite massive progenitor possibly resembling the giant clumps observed in star-forming galaxies at high redshifts. This connection opens a new route of investigation into the formation process and evolution of spheroids and their stellar content.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

A double peak in the seasonality of California's photosynthesis as observed from space

Solar-induced chlorophyll fluorescence (SIF) has been shown to be a powerful proxy for photosynthesis and gross primary productivity (GPP). The recently launched TROPOspheric Monitoring Instrument (TROPOMI) features the required spectral resolution and signal-to-noise ratio to retrieve SIF from space. Here, we present a downscaling method to obtain 500 m spatial resolution SIF over California. We report daily values based on a 14 d window. TROPOMI SIF data show a strong correspondence with daily GPP estimates at AmeriFlux sites across multiple ecosystems in California. We find a linear relationship between SIF and GPP that is largely invariant across ecosystems with an intercept that is not significantly different from zero. Measurements of SIF from TROPOMI agree with MODerate Resolution Imaging Spectroradiometer (MODIS) vegetation indices – the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation index (NIR$_v$) – at annual timescales but indicate different temporal dynamics at monthly and daily timescales. TROPOMI SIF data show a double peak in the seasonality of photosynthesis, a feature that is not present in the MODIS vegetation indices. The different seasonality in the vegetation indices may be due to a clear-sky bias in the vegetation indices, whereas previous work has shown SIF to have a low sensitivity to clouds and to detect the downregulation of photosynthesis even when plants appear green. We further decompose the spatiotemporal patterns in the SIF data based on land cover. The double peak in the seasonality of California's photosynthesis is due to two processes that are out of phase: grasses, chaparral, and oak savanna ecosystems show an April maximum, while evergreen forests peak in June. An empirical orthogonal function (EOF) analysis corroborates the phase offset and spatial patterns driving the double peak. The EOF analysis further indicates that two spatiotemporal patterns explain 84 % of the variability in the SIF data. Results shown here are promising for obtaining global GPP at sub-kilometer spatial scales and identifying the processes driving carbon uptake.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Metabolome patterns identify active dechlorination in bioaugmentation consortium SDC-9™

Ultra-high performance liquid chromatography–high-resolution mass spectrometry (UPHLC–HRMS) is used to discover and monitor single or sets of biomarkers informing about metabolic processes of interest. The technique can detect 1000’s of molecules (i.e., metabolites) in a single instrument run and provide a measurement of the global metabolome, which could be a fingerprint of activity. Despite the power of this approach, technical challenges have hindered the effective use of metabolomics to interrogate microbial communities implicated in the removal of priority contaminants. Herein, our efforts to circumvent these challenges and apply this emerging systems biology technique to microbiomes relevant for contaminant biodegradation will be discussed. Chlorinated ethenes impact many contaminated sites, and detoxification can be achieved by organohalide-respiring bacteria, a process currently assessed by quantitative gene-centric tools (e.g., quantitative PCR). This laboratory study monitored the metabolome of the SDC-9™ bioaugmentation consortium during cis-1,2-dichloroethene (cDCE) conversion to vinyl chloride (VC) and nontoxic ethene. Untargeted metabolomics using an UHPLC-Orbitrap mass spectrometer and performed on SDC-9™ cultures at different stages of the reductive dechlorination process detected ~10,000 spectral features per sample arising from water-soluble molecules with both known and unknown structures. Multivariate statistical techniques including partial least squares-discriminate analysis (PLSDA) identified patterns of measurable spectral features (peak patterns) that correlated with dechlorination (in)activity, and ANOVA analyses identified 18 potential biomarkers for this process. Statistical clustering of samples with these 18 features identified dechlorination activity more reliably than clustering of samples based only on chlorinated ethene concentration and Dhc 16S rRNA gene abundance data, highlighting the potential value of metabolomic workflows as an innovative site assessment and bioremediation monitoring tool.

environmental monitoring↗

DOE OSTI2016

SeekAView: An Intelligent Dimensionality Reduction Strategy for Navigating High-Dimensional Data Spaces

Dealing with the curse of dimensionality is a key challenge in high-dimensional data visualization. We present SeekAView to address three main gaps in the existing research literature. First, automated methods like dimensionality reduction or clustering suffer from a lack of transparency in letting analysts interact with their outputs in real-time to suit their exploration strategies. The results often suffer from a lack of interpretability, especially for domain experts not trained in statistics and machine learning. Second, exploratory visualization techniques like scatter plots or parallel coordinates suffer from a lack of visual scalability: it is difficult to present a coherent overview of interesting combinations of dimensions. Third, the existing techniques do not provide a flexible workflow that allows for multiple perspectives into the analysis process by automatically detecting and suggesting potentially interesting subspaces. In SeekAView we address these issues using suggestion based visual exploration of interesting patterns for building and refining multidimensional subspaces. Compared to the state-of-the-art in subspace search and visualization methods, we achieve higher transparency in showing not only the results of the algorithms, but also interesting dimensions calibrated against different metrics. We integrate a visually scalable design space with an iterative workflow guiding the analysts by choosing the starting points and letting them slice and dice through the data to find interesting subspaces and detect correlations, clusters, and outliers. We present two usage scenarios for demonstrating how SeekAView can be applied in real-world data analysis scenarios.

Krause, Josua↗

DOE OSTI2022

Detection of Forced Change Within Combined Climate Fields Using Explainable Neural Networks

Abstract Assessing forced climate change requires the extraction of the forced signal from the background of climate noise. Traditionally, tools for extracting forced climate change signals have focused on one atmospheric variable at a time, however, using multiple variables can reduce noise and allow for easier detection of the forced response. Following previous work, we train artificial neural networks to predict the year of single‐ and multi‐variable maps from forced climate model simulations. To perform this task, the neural networks learn patterns that allow them to discriminate between maps from different years—that is, the neural networks learn the patterns of the forced signal amidst the shroud of internal variability and climate model disagreement. When presented with combined input fields (multiple seasons, variables, or both), the neural networks are able to detect the signal of forced change earlier than when given single fields alone by utilizing complex, nonlinear relationships between multiple variables and seasons. We use layer‐wise relevance propagation, a neural network explainability tool, to identify the multivariate patterns learned by the neural networks that serve as reliable indicators of the forced response. These “indicator patterns” vary in time and between climate models, providing a template for investigating inter‐model differences in the time evolution of the forced response. This work demonstrates how neural networks and their explainability tools can be harnessed to identify patterns of the forced signal within combined fields.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Phylodynamics of SARS-CoV-2 Lineages B.1.1.7, B.1.1.529 and B.1.617.2 in Nigeria Suggests Divergent Evolutionary Trajectories

Background: The early months of the COVID-19 pandemic were characterized by high transmission rates and mortality, compounded by the emergence of multiple SARS-CoV-2 lineages, including Variants of Concern (VOCs). This study investigates the phylodynamic and spatio-temporal trends of VOCs during the peak of the pandemic in Nigeria. Methods: Whole-genome sequencing (WGS) data from three major VOCs circulating in Nigeria, B.1.1.7 (Alpha), B.1.617.2 (Delta), and B.1.1.529 (Omicron), were analyzed using tools such as Nextclade, R Studio v 4.2.3, and BEAST X v 10.5.0. The spatial distribution, evolutionary history, viral ancestral introductions, and geographic dispersal patterns were characterized. Results: Three major lineages following WHO nomenclature were identified: Alpha, Delta, and Omicron. The Delta variant exhibited the widest geographic spread, detected in 14 states, while the Alpha variant was the least distributed, identified in only eight states but present across most epidemiological weeks studied. Evolutionary rates varied slightly, with Alpha exhibiting the slowest rate (2.66 × 10 −4 substitutions/site/year). Viral population analyses showed distinct patterns: Omicron sustained elevated population growth over time, while Delta declined after initial expansion. The earliest Times to Most Recent Common Ancestor (TMRCA) were consistent with the earliest outbreaks of SARS-CoV-2 globally. Geographic transmission analysis indicated a predominant coastal-to-inland spread for all variants, with Omicron showing the most diffuse dispersal, highlighting commercial routes as significant drivers of viral diffusion. Conclusion: The SARS-CoV-2 epidemic in Nigeria was characterized by multiple variant introductions and a dominant coastal-to-inland spread, emphasizing that despite lockdown measures, commercial trade routes played a critical role in viral dissemination. These findings provide insights into pandemic control strategies and future outbreak preparedness.

Nigeria↗

DOE OSTI2020

Doubling of U.S. Population Exposure to Climate Extremes by 2050

We quantify historical and projected trends in the population exposure to climate extremes as measured by the United States National Center for Environmental Information Climate Extremes Index (CEI). Based on the analyses of the historical observations, we find that the U.S. has already experienced a rise in the occurrence of aggregated extremes in recent decades, consistent with the climate response to historical increases in radiative forcing. Additionally, we find that exposure can be expected to intensify under the Representative Concentration Pathway 8.5, with all counties permanently exceeding the baseline variability in the occurrence of extreme hot days, warm nights, and drought conditions by 2050. As a result, every county in the U.S. is projected to permanently exceed the historical CEI variability (as measured by one standard deviation during the 1981–2005 period). Based on the current population distribution, this unprecedented change implies a yearly exposure to extreme conditions for one in every three people. We find that the increasing trend in exposure to the aggregated extremes is already detectable over much of the U.S., and particularly in the central and eastern U.S. The high correspondence between the pattern of trends in our simulations and observations increases confidence in the projected amplification of population exposure to unprecedented combinations of extreme climate conditions, should greenhouse gas concentrations continue to escalate along their current trajectory.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Superparticle phenomenology from the natural mini-landscape

The methodology of the heterotic mini-landscape attempts to zero in on phenomenologically viable corners of the string landscape where the effective low energy theory is the Minimal Supersymmetric Standard Model with localized grand unification. The gaugino mass pattern is that of mirage-mediation. The magnitudes of various SM Yukawa couplings point to a picture where scalar soft SUSY breaking terms are related to the geography of fields in the compactified dimensions. Higgs fields and third generation scalars extend to the bulk and occur in split multiplets with TeV scale soft masses. First and second generation scalars, localized at orbifold fixed points or tori with enhanced symmetry, occur in complete GUT multiplets and have much larger masses. This picture can be matched onto the parameter space of generalized mirage mediation. Naturalness considerations, the requirement of the observed electroweak symmetry breaking pattern, and LHC bounds on m(gluino) together limit the gravitino mass to the $m_{3/2}~ 5-60$ TeV range. The mirage unification scale is bounded from below with the limit depending on the ratio of squark to gravitino masses. We show that while natural SUSY in this realization may escape detection even at the high luminosity LHC, the high energy LHC with $\sqrt{s}=33$ TeV could unequivocally confirm or exclude this scenario. It should be possible to detect the expected light higgsinos at the ILC if these are kinematically accessible, and possibly also discriminate the expected compression of gaugino masses in the natural mini-landscape picture from the mass pattern expected in models with gaugino mass unification. The thermal WIMP signal should be accessible via direct detection searches at the multi-ton noble liquid detectors such as Xenon-nT or LZ.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Investigation of Multiple Data Streams for Gearbox Bearing Fault Prediction Through Machine-Learning Models

Operations and maintenance (O&M) cost of wind plant accounts up to 30% of total energy cost, which can be reduced through continuous monitoring and successfully detecting incipient wind turbine failures. To accomplish this, condition monitoring and predictive maintenance systems are being implemented in wind industry to support O&M decision making. A wide range of approaches for condition monitoring and fault prediction have been developed. These approaches generally use historical data of wind turbines collected by Supervisory Control and Data Acquisition (SCADA) system to identify patterns that lead to failure. These SCADA data show the overall condition of a wind turbine and can be leveraged to detect when the turbine's performance is degrading and to identify if a fault is developing. However, it becomes challenging to predict the failure of a specific wind turbine gearbox bearing, because the SCADA data are often not directly linked to the component. To bridge the gap, we have investigated features calculated from SCADA data using physics-based models and the gearbox design over the years. The damaged metric we used in the physics domain is frictional energy. Combining these physics domain variables with SCADA data as inputs to various machine learning models for gearbox bearing fault prediction, we have demonstrated the benefits of leveraging both physics and data domain models. It was an attempt to improve frictional-energy-based damage metric by adding data domain inputs, as we had learned that the frictional-energy-based damage metric alone is not sufficient to single out failed bearings from healthy. As condition monitoring data (either vibration or oil debris data) has become available at more and more wind plants, we would like to evaluate whether by adding the condition monitoring data can help further improve the performance of frictional-energy-based damage metric for gearbox bearing fault prediction. Both cases by modeling through various machine learning algorithms are discussed in this study along with some observations.

fault prediction↗

DOE OSTI2020

Application of multidimensional structural characterization to detect and describe moderate forest disturbance

The study of vegetation community and structural change has been central to ecology for over a century, yet the ways in which disturbances reshape the physical structure of forest canopies remain relatively unknown. Moderate severity disturbances affect different canopy strata and plant species, resulting in variable structural outcomes and ecological consequences. Terrestrial lidar (light detection and ranging) offers an unprecedented view of the interior arrangement and distribution of canopy elements, permitting the derivation of multidimensional measures of canopy structure that describe several canopy structural traits (CSTs) with known links to ecosystem function. We used lidar-derived CSTs within a machine learning framework to detect and describe the structural changes that result from various disturbance agents, including moderate severity fire, ice storm damage, age-related senescence, hemlock woolly adelgid, beech bark disease, and chronic acidification. We found that fire and ice storms primarily affected the amount and position of vegetation within canopies, while acidification, senescence, pathogen, and insect infestation altered canopy arrangement and complexity. Only two of the six disturbance agents significantly reduced leaf area, counter to common assumptions regarding many moderate severity disturbances. While findings are limited in their generalizability due to lack of replication among disturbances, they do suggest that the current limitations of standard disturbance detection methods—such as optical-based remote sensing platforms, which are often above-canopy perspectives—limit our ability to understand the full ecological and structural impacts of disturbance, and to evaluate the consistency of structural patterns within and among disturbance agents. A more broadly inclusive definition of ecological disturbance that incorporates multiple aspects of canopy structural change may potentially improve the modeling, detection, and prediction of functional implications of moderate severity disturbance as well as broaden our understanding of the ecological impacts of disturbance.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Evaluation of alkali–silica reaction damage in concrete using linear and nonlinear resonance techniques

Here in this study, the initiation and development of alkali–silica reaction (ASR) in concrete prisms containing reactive coarse (RCA) and reactive fine (RFA) aggregates were monitored over one year using linear and nonlinear resonance techniques. Although the linear resonance test could detect initiation and development of ASR damage in RCA specimens, it failed in the RFA specimens. Petrographic analysis confirmed the presence of early-stage ASR damage and the presence of different microcracking patterns in the RFA and RCA specimens, which may explain differences in the linear resonance data. For both RCA and RFA specimens, the nonlinear resonance test results were able to detect the initiation of ASR damage and showed a strong correlation with the ASR expansion for all specimens from different mix designs. The results demonstrate the high sensitivity of the nonlinear resonance test and its potential for quantitative evaluation of the ASR damage state of concrete specimens without a baseline measurement.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Multifunctionality of nanocrystalline lanthanum ferrite

Nanocrystalline lanthanum ferrite has been synthesized by adopting modified Pechini route. No evidence of impurity or secondary phase has been detected up to the detection of error limit of X-ray diffractometer (XRD). Rietveld refinement of X-ray diffraction pattern reveals orthorhombic crystal system with space group Pnma (62).Crystallite size and lattice strain was found to be ∼42.8nm and 0.306% respectively. Optical band gap was found to be 2.109 eV, by UV-Visible diffused reflectance spectrum (DRS). Brunauer-Emmet-Teller (BET) surface area was found to be ∼3.45 m{sup 2}/g. Magnetization-hysteresis (M-H) loop was recorded at room temperature (300K) reveals weak ferromagnetism in Nanocrystalline lanthanum ferrite. The weak ferromagnetism in lanthanum ferrite is due to the uncompensated antiferromagnetic spin ordering. Ferroelectric loop hysteresis observed at room temperature at 100Hz depicts the presence of ferroelectric ordering in LaFeO{sub 3}.Simultanious presence of magnetic and ferroelectric ordering at room temperature makes it suitable candidate of Multiferroic family.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗