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At least 91 records · Page 5

Mapping Distant Continental Influences in the Remote Pacific Atmosphere; Simulations of CO Relevant to the Photochemistry of Oxidants

An animated sequence of maps of simulated carbon monoxide concentrations graphically portrays the extent of residual continental influence upon the tropical Pacific Ocean as studied by NASA aircraft during the PEM-Tropics B intensive sampling campaign. We used the MM5 at a 90 km resolution in a globally wrapped grid to simulate the meteorology of transport, and our GRACES model to follow the basic chemistry. The CO we simulate derives from different sources, and so we distinguish anthropogenic, natural terpenoid oxidation, biomass burning, and pervasive CH4-oxidation influences. "Influence" is always judged with an implicit timescale, and these maps describe influence on the 15-45 day timescale appropriate for CO oxidation. In consequence, the maps are useful in assessing the origins of slowly reacting compounds like acetone, methanol, and the lightest hydrocarbons. At 8 km altitude, The Eastern South Pacific to ca. 130 W (eastern Polynesia) was frequently affected by continental influences but NASA's DC-8's flight path did not happen to take it into these regions very often. Near the surface, continentally influenced air crossed into t he Western South Pacific, in the region northwest of the Southern Pacific Convergence Zone but south of the Intertropical Convergence Zone. This air originated from the NE Pacific, and partly from North America. Comparisons are made to CO and other compounds measured aboard the DC-8 and the P-3 aircraft. We will also use tracers to describe the influence of marine convection in the upper troposphere. As time allows, we will discuss the "age" of ozone within the very cleanest region sampled in portions of the near-equatorial Western South Pacific, using a simple chemical mechanism for ozone levels. These simulations describe the chemistry of an atmosphere with very low ozone.

Chatfield Robert B.↗

Factors Affecting Bait Site Visitation: Area of Influence of Baits

Baiting is a fundamental strategy for the global management of wild pigs (Sus scrofa); however, little information exists on how anthropogenic bait affects wild pig movements on a landscape. We investigated factors that are important in determining the spatial area of attraction for wild pigs to bait (‘area of influence’ of a bait site) using data from Global Positioning System (GPS) collars and locations of bait sites. We monitored movements of wild pigs in 2 distinct study areas in the United States from February to September 2016 and used locational data using GPS collars to analyze the influence of habitat quality (dependent on site), home range size, number of bait sites in the home range, distance to a bait site, and sex in relation to movement in time and space. We determined the average area of influence by calculating the area of a circle with the radius as the average maximum distance travelled by wild pigs to reach a bait site. The average area of influence for our bait sites was 6.7 km 2 (or a radius of approximately 1.5 km), suggesting a bait spacing of approximately 1.5 km would be adequate to capture visitation by most wild pigs and a spacing of 3 km could allow substantial visitation while minimizing redundant effort depending on the spatial structure of the populations. Eighty percent of wild pigs first visited bait sites within 8.9 days after bait deployment; and they visited earlier when their home range size was larger. As the number of bait sites in an individual's home range increased, individual pigs visited more bait sites, and the probability of a visit increased dramatically up to approximately 5 bait sites and much less thereafter. Wild pigs travelled farther distances to visit bait sites in lower quality habitat. Our results support the hypothesis that habitat quality can mediate the efficacy of baiting programs for wildlife by influencing their movement patterns and motivation to use anthropogenic resources. Our results suggest wild pigs will travel extensively within their home range to visit bait sites, and that in lower quality habitat, most animals will find bait sites more quickly. Determining the area of influence of bait sites can increase the efficacy of planning and monitoring management programs. Overall, our study provides new information to help managers plan baiting designs to attract the greatest number of pigs.

59 BASIC BIOLOGICAL SCIENCES↗

Evolution of Intent and Social Influence Networks and Their Significance in Detecting COVID-19 Disinformation Actors on Social Media

Online disinformation actors are those individuals or bots who disseminate false or misleading information over social media, with the intent to sway public opinion in the information domain towards harmful social outcomes. Quantification of the degree to which users post or respond intentionally versus under social influence, remains a challenge, as individuals or organizations operating the profile are foreshadowed by their online persona. However, social influence has been shown to be measurable in the paradigm of information theory. In this paper, we introduce an information theoretic measure to quantify social media user intent, and then investigate the corroboration of intent with evolution of the social network and detection of disinformation actors related to COVID-19 discussions on Twitter. Our measurement of user intent utilizes an existing time series analysis technique for estimation of social influence using transfer entropy among the considered users. We have analyzed 4.7 million tweets originating from several countries of interest, during a 5 month period when the arrival of the first dose of COVID vaccinations were announced. Our key findings include evidence that: (i) a significant correspondence between intent and social influence; (ii) ranking over users by intent and social influence is unstable over time with evidence of shifts in the hierarchical structure; and (iii) both user intent and social influence are important when distinguishing disinformation actors from non-disinformation actors.

Gunaratne, Chathika↗

Influences of pH and substrate supply on the ratio of iron to sulfate reduction

Iron reduction and sulfate reduction often occur simultaneously in anoxic systems, and where that is the case, the molar ratio between the reactions (i.e., Fe/SO 4 2- reduced) influences their impact on water quality and carbon storage. Previous research has shown that pH and the supply of electron donors and acceptors affect that ratio, but it is unclear how their influences compare and affect one another. This study examines impacts of pH and the supply of acetate, sulfate, and goethite on the ratio of iron to sulfate reduction in semi-continuous sediment bioreactors. We examined which parameter had the greatest impact on that ratio and whether the parameter influences depended on the state of each other. Results show that pH had a greater influence than acetate supply on the ratio of iron to sulfate reduction, and that the impact of acetate supply on the ratio depended on pH. In acidic reactors (pH 6.0 media), the ratio of iron to sulfate reduction decreased from 3:1 to 2:1 as acetate supply increased (0-1 mM). In alkaline reactors (pH 7.5 media), iron and sulfate were reduced in equal proportions, regardless of acetate supply. Secondly, a comparison of experiments with and without sulfate shows that the extent of iron reduction was greater if sulfate reduction was occurring and that the effect was larger in alkaline reactors than acidic reactors. Thus, the influence of sulfate supply on iron reduction extent also depended on pH and suggests that iron reduction grows more dependent on sulfate reduction as pH increases. Our results compare well to trends in groundwater geochemistry and provide further evidence that pH is a major control on iron and sulfate reduction in systems with crystalline (oxyhydr)oxides. pH not only affects the ratio between the reactions but also the influences of other parameters on that ratio.

59 BASIC BIOLOGICAL SCIENCES↗

Factors That Influence Variability in Stress-Drop Measurements Using Spectral Decomposition and Spectral-Ratio Methods for the 2019 Ridgecrest Earthquake Sequence

Stress drop is a fundamental parameter related to earthquake source physics, but is hard to measure accurately. To better understand how different factors influence stress-drop measurements, we compare two different methods using the Ridgecrest stress-drop validation data set: spectral decomposition (SD) and spectral ratio (SR), each with different processing options. Here, we also examine the influence of spectral complexity on source parameter measurement. Applying the SD method, we find that frequency bandwidth and time-window length could influence spectral magnitude calibration, while depth-dependent attenuation is important to correctly map stress-drop variations. For the SR method, we find that the selected source model has limited influence on the measurements; however, the Boatwright model tends to produce smaller standard deviation and larger magnitude dependence than the Brune model. Variance reduction threshold, frequency bandwidth, and time-window length, if chosen within an appropriate parameter range, have limited influence on source parameter measurement. For both methods, wave type, attenuation correction, and spectral complexity strongly influence the result. The scale factor that quantifies the magnitude dependence of stress drop show large variations with different processing options, and earthquakes with complex source spectra deviating from the Brune-type source models tend to have larger scale factor than earthquakes without complexity. Based on these detailed comparisons, we make a few specific suggestions for data processing workflows that could help future studies of source parameters and interpretations.

58 GEOSCIENCES↗

Climate Influences on Capacity Expansion Planning with Application to the Western U.S

Electric power system planners utilize a variety of planning tools to inform decisions concerning generation and transmission additions to the electric grid, the need for operational changes, and to evaluate potential stressors on the system. Numerous factors contribute to the planning process including projected fuel and technology costs, policy and load profiles. There is also a growing recognition of the interdependency of the electric grid with other natural and engineered systems. Here we explore how future climate change and hydropower operability might influence decisions related to electricity capacity expansion planning and operations. To do so we assemble a multi-model framework. Specifically, water resource modeling is used to simulate climate impacts on future water supply for thermoelectric and hydropower generation. Separately, temperature impacts on electricity load are evaluated. Together, these climate factors spatially constrain a capacity expansion model that projects generation and transmission additions to the grid. The projected new capacity-builds are then evaluated on their operations, reliability, and cost under average and extreme climate conditions using production cost modeling. This coupled framework is demonstrated on the electric grid in the Western U.S., supporting capacity expansion planning by WECC, the North American Electric Reliability Corporation (NERC) regional entity responsible for reliability assurance of the Western Interconnection. This region was selected in part because the West is unique in that it has high potential for renewable penetrations and is experiencing large retirements/displacements of baseload resources, primarily coal, leading to possible operational challenges in terms of changing resource mix and the need for resource flexibility. Toward this challenge, planning scenarios encompass a range of alternative energy, climate and drought futures. In this context we explore answers to two strategic questions: 1) How does changing climate influence electricity expansion planning (generation and transmission) and future operations, including type and capacity of new builds, system reliability, cost and environmental impacts? 2) How does the representation of hydropower in the modeling framework influence the evaluation of bulk power system operations? Results indicate that climate has a measurable influence on recommendations concerning the capacity, type and location of new generation and transmission additions, with up to 17 GW additional capacity needed by 2038 to meet peak loads (~6.6% increase over capacity-builds based on historical climate). The extent of additional infrastructure needs is strongly influenced by future water availability for hydropower and the potential deployment of demand response technologies. Systems designed for future climate conditions were found to maintain high system reliability under a range of electricity and water availability scenarios (including significant drought), with minimal system curtailments. Additional capacity needs due to higher load tend to increase cumulative 20-year investment and operating costs by $\$$5-$\$$17 billion and generation costs increase by 9 to 19%. Finally, changing the representation of hydropower flexibility has a relatively small influence on capacity expansion in the Western Interconnection through 2038, but hydropower flexibility impacts generation costs to a similar extent as climate.

13 HYDRO ENERGY↗

The influence of defects and impurities on the nucleation and growth of oriented films by evaporation

The influence of substrate imperfections on the nucleation and growth of fcc metals on alkali halides is discussed. Films deposited on well characterized substrated under well defined vacuum evaporation conditions are investigated. The experimental results of this work are correlated with similar work by other investigators. Models which have been proposed by various authors to explain experimental results are critically examined and areas of difficulty are pointed out. The influence of defects on nucleation rate and the orientation of the film is emphasized. Specific examples of impurity effects, irradiation effects and the influence of amorphous layers are discussed in detail. Evidence is shown that the formation of multiply twinned particles is a result of coalescence and growth. The only consistent model for the orienting influence of impurities is shown to be a chemical reaction effect. It is demonstrated that an alkali metal impurity is very likely responsible for the orienting influence of both water vapor exposure and irradiation. A negative result is found for the reported possibility of an orienting influence being transmitted through an amorphous layer.

Green, A. K.↗

Irradiating of Bulk Soybeans: Influence on Their Functional and Sensory Properties for Soyfood Processing

Soybeans were chosen for lunar and planetary missions, where soybeans will be supplied in bulk or grown locally, due to their nutritive value and ability to produce oil and protein for further food applications. However, soybeans must be processed into foods prior to consumption. Radiation that soybeans would be exposed to during bulk storage prior to and during a Mars mission may influence their germination and functional properties. The influence of radiation includes the affect of surface pasteurization to ensure the astronauts safety from food-borne illnesses (HACCP, CCP), and the affect of the amount of radiation the soybeans receive during a Mars mission. Decreases in the amount of natural antioxidants free radical formation, and oxidation-induced changes in the soybean will influence the nutritional value, texture, color, and aroma of soyfoods. The objective of this study was to determine the influence of pasteurization and sterilization surface radiation on whole soybeans using gamma and electron beam radiation. The influence of 0, 1, 5, 10, and 30kGy on microbial load, germination rate, ease of processing, and quality of soymilk and tofu were determined. Surface radiation of whole dry soybeans using electron beam or gamma rays from 1-30kGy did provide microbial safety for the astronauts. However, the lower dose levels had surviving yeasts and molds. These doses caused oxidative changes that resulted in soymilk and tofu with rancid aromas. GC-MS of the aroma compounds using SPME Headspace confirmed the presence of lipid oxidation compounds. Soybean germination ability was reduced as radiation dosage increased. While lower doses may reduce these problems, the ability to insure microbial safety of bulk soybeans will be lost. Counter measures could include vacuum packaging, nitrogen flushing, added antioxidants, and radiating under freezing conditions. Doses below 1kGy need to be investigated further to determine the influence of the radiation encountered during Mars missions.

Chia, Chiew-Ling↗

The Impact (Influence) of Stakeholder Participation in the Logic Modeling Process: A Case Study from NASA Goddard Space Flight Center

This is a three-paper dissertation that responds to a call for empirical research on stakeholder influence in the implementation of evaluation recommendations. The literature is consistent but mostly theoretical: stakeholder participation should positively influence and increase the probability of implementation (see e.g., Christie, 2011; Cousins and Earl, 1992); Fetterman, 2004). In response, I use two Goddard Space Flight Center engineering technical training and development programs to build a causal pathway argument supporting my proposition that stakeholder influence in the logic modeling process impacts organizational decision-making and change. I first introduce the logic modeling process as a tool to involve stakeholders in program and evaluation design (paper 1), then use the data collection process to implement the collaboratively constructed logic model outcomes (paper 2), and finally, I triangulate the data using the Toal (2009) Evaluation Involvement Scale (paper 3). I do this to “increase the validity of the findings” (Noble & Heale, 2019, p. 67) and to garner potential mechanisms. I found that stakeholder participation in the logic modeling process positively influenced the use of data to inform recommendations which in turn positively influenced implementation of evaluation recommendations. I identified the process itself as the mechanism and the key decision points as links or influencers. Potential factors included power and interest (measured by role) and time-on-job.

Organizational Decision-Making↗

Are You In or Out: the Influence of Sea Ice Drift on Sea Ice Survivability

The state of Arctic sea ice influences aspects of many global systems including ecosystems, economies, geopolitics, and climate. And, it is in severe decline. Numerous studies have considered the factors that influence Arctic sea ice decline using monthly mean or gridded data. However, these methods struggle to cleanly separate the influences on sea ice melt because the sea ice cover is always moving; thus, different sea ice floes and sea ice regimes constitute the monthly average sea ice state within the Eulerian frame. This study uses a LaGrangian sea ice parcel tracking satellite database to investigate the factors that influence sea ice parcel survivability. The concept of sea ice survivability is defined as the likelihood that a sea ice parcel will last through the summer melt season. Survivability can be stratified in many ways to analyze how it changes as a function of sea ice regime, region, and sea ice characteristics. How is the survivability of first year (FY) and multi-year (MY) ice classes influenced by sea ice motion? This is the central question addressed in this presentation. Specifically, this analysis determines sea ice survivability as a function of distance traveled by sea ice parcels and compares the survivability statistics for sea ice parcels that stay in their starting region versus those that leave their starting region. The preliminary results indicate that the influence of sea ice movement on survivability depends strongly on which region the sea ice resides in at the beginning of the growth season. Lastly, we find substantial inter-annual variability in the regional distribution of sea ice survivability that seems to be strongly linked to sea ice parcel drift between regions of the Arctic Ocean. Moreover, we use these results to discuss the potential contribution of changes in sea ice motion on the observed slowdown in Arctic sea ice extent loss.

Patrick C Taylor↗

An influence of manufacturing tolerances on Pin-Cell k-infinity of MOX fuel using data from the FUBILA experiment program

An influence of manufacturing tolerances (MTs) was evaluated on the pin-cell k-infinity of MOX fuel through the random sampling of CASMO5 calculations. The data from the FUBILA experiment program was used as the manufacturing parameters. For the uncertainties of element/isotope mass fractions, their covariance matrices were calculated by the generalized least square method. The total k-infinity uncertainty was 120-250 pcm (percent mille). From the breakdown of k-infinity uncertainty per materials, the MTs of fuel pellet had a dominant influence. The individual influences were also evaluated for element/isotope mass fractions, an inner/outer diameter, and a density. Those of element/isotope mass fractions were less than a few dozen pcm. Since the perturbations of fuel pellet diameter and density caused large variations to the total amount of heavy metals and effect of spatial self-shielding, they had a negative correlation with the k-infinity perturbation. On the AG3 (Al-Mg alloy) over-cladding, the inner/outer diameter perturbations had both negative and positive correlations. The negative one was due to decrease in amount of light water and the positive one was due to increase in amount of AG3. Since the effect of neutron slowing-down by light water had a dominant influence, the influence of the former negative correlation was larger than that of the latter positive correlation. It is, therefore, concluded that the consideration of the MTs that had a large influence on the effect of neutron slowing-down is important for precise quantification of the pin-cell k-infinity uncertainty of MOX fuel. (author)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Quantifying the influence of different biocrust community states and their responses to warming temperatures on soil biogeochemistry in field and mesocosm studies

Biocrusts influence soil biogeochemistry by fixing carbon (C) and nitrogen (N) and through leachate inputs to soils. Functional rates can vary among biocrust community states and in response to edaphic properties, heterotrophic microbial activity, and global change. Using soils and biocrusts from the Colorado Plateau, Utah, USA, we aimed to quantify the influence of early-successional (ES) and late-successional (LS) biocrust community states on soil biogeochemistry. In a field setting, we found soil was less “fertile” under ES than LS biocrust, but ES biocrust had a relative influence 1.3 times greater than LS biocrust on soil fertility. Leachate collected from LS biocrust had, on average, 6 times more organic C and 1.7 times more dissolved N than ES biocrust, but concentrations of phosphorus (P) and inorganic N did not differ among the two biocrust types. Here, to disentangle influences of biocrusts and soil properties on biogeochemical pools, we constructed mesocosms from homogenized soil and left the surface bare or covered with ES or LS biocrust, before assignment to ambient or warmed (+5 °C) temperature treatments for 3 months. Multivariate biogeochemical properties differed among cover types, yet all exhibited losses of P, N, and organic C and nearly half of the biogeochemical variables considered did not differ among cover types. Mesocosms with LS biocrust retained more dissolved N, supported 8 additional, significant correlations among biogeochemical pools of the biocrust and mineral soil layer on average, and lost fewer of these correlations under warming. Overall, while soils under LS biocrusts were more fertile (i.e., had higher nutrient concentrations) than under ES, we did not find evidence implicating leachate as the primary driver of this difference. Biocrust influences on soil fertility were greater when mineral soil nutrients were in lower concentrations, highlighting the value of even incipient biocrusts for dryland functioning.

54 ENVIRONMENTAL SCIENCES↗

Tropical Indian Ocean Mediates ENSO Influence Over Central Southwest Asia During the Wet Season

El Niño–Southern Oscillation (ENSO) modulates wet season (November–April) precipitation over Central Southwest Asia (CSWA), however, intraseasonal characteristics of its influence are largely unknown, which can be important for its subseasonal to seasonal hydroclimate predictability. Here we show that the ENSO-CSWA teleconnection varies intraseasonally and is a combination of direct and indirect positive influences. The direct influence is through a Rossby wave-like pattern in the tail months. The indirect influence is through an atmospheric dipole of diabatic heating anomalies in the tropical Indian Ocean (TIO) as a result of ENSO-forced response, which also generates a Rossby wave-like forcing and persists throughout the wet season. ENSO exerts its strongest influence when both direct and indirect modes are in phase, while the relationship breaks down when the two modes are out of phase. The atmospheric teleconnection through the atmospheric diabatic heating anomalies in the TIO is reproducible in numerical simulations.

54 ENVIRONMENTAL SCIENCES↗

Constructing tensor network influence functionals for general quantum dynamics

Here, we describe an iterative formalism to compute influence functionals that describe the general quantum dynamics of a subsystem beyond the assumption of linear coupling to a quadratic bath. We use a space-time tensor network representation of the influence functional and investigate its approximability in terms of its bond dimension and time-like entanglement in the tensor network description. We study two numerical models, the spin-boson model and a model of interacting hard-core bosons in a 1D harmonic trap. We find that the influence functional and the intermediates involved in its construction can be efficiently approximated by low bond dimension tensor networks in certain dynamical regimes, which allows the quantum dynamics to be accurately computed for longer times than with direct time evolution methods. However, as one iteratively integrates out the bath, the correlations in the influence functional can first increase before decreasing, indicating that the final compressibility of the influence functional is achieved via non-trivial cancellation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Statistical Influence

Advancing the spread and practice of statistics enhances an organization’s ability to successfully achieve their mission. While there may be leadership mandates to employ statistical methods, often the spread of statistical concepts flourishes more effectively through the practice of statistical influence. At first glance, the term influence may seem to imply a passive and unenthusiastic posture toward promoting organizational change. However, a classical definition states that ,“In a general sense, influence denotes power whose operation is invisible and known only by its effects, or a power whose cause and operation are unseen.” This definition articulates the powerful and yet subtle aspects of influence that embodies the theme of this presentation. Stated plainly, powerful statistical concepts become more widely known and engrained primarily through demonstrated impact; a strategy known only by its effects. In this presentation, elements of statistical influence are exemplified through practice at NASA.

Peter A. Parker↗

GraMeR: Gra ph Me ta R einforcement learning for multi-objective influence maximization

Influence maximization (IM) is a combinatorial problem of identifying a subset of seed nodes in a network (graph), which when activated, provide a maximal spread of influence in the network for a given diffusion model and a budget for seed set size. IM has numerous applications such as viral marketing, epidemic control, sensor placement and other network-related tasks. However, its practical uses are limited due to the computational complexity of current algorithms. Recently, deep reinforcement learning has been leveraged to solve IM in order to ease the computational burden. However, there are serious limitations in current approaches, including narrow IM formulation that only consider influence via spread and ignore self-activation, low scalability to large graphs, and lack of generalizability across graph families leading to a large running time for every test network. In this work, we address these limitations through a unique approach that involves: (1) Formulating a generic IM problem as a Markov decision process that handles both intrinsic and influence activations; (2)incorporating generalizability via meta-learning across graph families. There are previous works that combine deep reinforcement learning with graph neural network, but this work solves a more realistic IM problem and incorporates generalizability across graphs via meta reinforcement learning. Extensive experiments are carried out in various standard networks to validate performance of the proposed Graph Meta Reinforcement learning (GraMeR) framework. Finally, the results indicate that GraMeR is multiple orders faster and generic than conventional approaches when applied on small to medium scale graphs.

97 MATHEMATICS AND COMPUTING↗

Donald J. Trump’s Presidency in Cyberspace: A Case Study of Social Perception and Social Influence in Digital Oligarchy Era

In the past few years, with the rapid growth of digital technologies, Facebook, Twitter, and other social media platforms have become the digital oligarchies, which have the enormous capabilities to potentially control what is discussed in cyberspace. In the digital oligarchy era, social perception and social influence in different complex social systems have evolved quickly. In this article, we conducted large-scale empirical studies on social perception and social influence regarding the Trump phenomenon from personal perception, media, and public attention perspectives. We found that there exist obvious correlations between the posting behavior of Trump and the attention of news media. By constructing public attention networks using complex networks based on Google search information, we further reveal that digital platforms could affect social perception and social influence significantly. Especially, we obtained that the public attention can always be influenced by the political moments.

digital oligarchy↗

Dynamic Network Analysis of Nuclear Science Literature for Research Influence Assessment

Analyzing nuclear science literature via data-driven methods is a critical step for assessing research influence and technology advancements. Indicators of scholarly activities may be buried in large volumes of nuclear research publications and collaboration networks over time. Mining for relevant scholarly influence trends in large volumes of text can be computationally challenging; however, open-source information on research collaborations over time can offer opportunities to extract meaningful insights. While network centrality analysis of scholarly research provides topology-based insights, additional emphasis on dynamics associated with the diffusion of information through these networks is important. Here this paper represents a step in that direction through the development of a novel dynamic network analysis framework and computational engine to identify key entities and capabilities over time within global scholarly nuclear science collaboration networks. Network theoretic, stochastic simulation, and optimization methods are leveraged to address variability in scholarly interactions, influence propagation, and collaboration patterns via network connections. A topic-aware influence maximization algorithm is developed to address the goal of identifying key influential authors in diverse research topics over time. Efficient parallelized implementation of the algorithm is applied to reduce computational costs. A proof-of-concept case study using open-source Scopus data with 33,517 published nuclear research papers from 2000-2019 is presented and representative analytic insights are generated. Broad implications of these insights are discussed and future research directions are also identified.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗