Large Cascade Research Articles

Predicting radiation damage in beryllium

ABSTRACT Displacement damage in beryllium was predicted as a function of temperature and energy using molecular dynamics simulations. A key aim of this study was to determine if average results from large displacement cascades correspond to values predicted by the Kinchin–Pease (K–P) model. The number of residual defects remaining after 1 ps increased linearly with primary knock-on atom (PKA) energy from 0.5 keV to 2.5 keV, while the extent of residual damage was largely temperature independent from 300 K to 1100 K. The same simulation model was used to predict the directionally averaged probability of displacement as a function of displacement energy, , and thereby the threshold displacement energy at which the probability for displacement is 100%, eV. There is an excellent correspondence between the K–P prediction using and the number of residual defects remaining after the initial recovery phase. Also, by utilising , a modification to the K–P model is proposed that gives rise to an average model prediction when .

Collateral Unchained: Rehypothecation Networks, Concentration and Systemic Effects

We study how network structure affects the dynamics of collateral in presence of rehypothecation. We build a simple model wherein banks interact via chains of repo contracts and use their proprietary collateral or re-use the collateral obtained by other banks via reverse repos. In this framework, we show that total collateral volume and its velocity are affected by characteristics of the network like the length of rehypothecation chains, the presence or not of chains having a cyclic structure, the direction of collateral flows, the density of the network. In addition, we show that structures where collateral flows are concentrated among few nodes (like in core-periphery networks) allow large increases in collateral volumes already with small network density. Furthermore, we introduce in the model collateral hoarding rates determined according to a Value-at-Risk (VaR) criterion, and we then study the emergence of collateral hoarding cascades in different networks. Our results highlight that network structures with highly concentrated collateral flows are also more exposed to large collateral hoarding cascades following local shocks. These networks are therefore characterized by a trade-off between liquidity and systemic risk.

Intervention Scenarios to Enhance Knowledge Transfer in a Network of Firms

We investigate a multi-agent model of firms in an R\&D network. Each firm is characterized by its knowledge stock $x_{i}(t)$, which follows a non-linear dynamics. It can grow with the input from other firms, i.e., by knowledge transfer, and decays otherwise. Maintaining interactions is costly. Firms can leave the network if their expected knowledge growth is not realized, which may cause other firms to also leave the network. The paper discusses two bottom-up intervention scenarios to prevent, reduce, or delay cascades of firms leaving. The first one is based on the formalism of network controllability, in which driver nodes are identified and subsequently incentivized, by reducing their costs. The second one combines node interventions and network interventions. It proposes the controlled removal of a single firm and the random replacement of firms leaving. This allows to generate small cascades, which prevents the occurrence of large cascades. We find that both approaches successfully mitigate cascades and thus improve the resilience of the R\&D network.

Mechanistic insights into the role of large carnivores for ecosystem structure and functioning

Large carnivores can exert top–down effects in ecosystems, but the size of these effects are largely unknown. Empirical investigation on the importance of large carnivores for ecosystem structure and functioning presents a number of challenges due to the large spatio‐temporal scale and the complexity of such dynamics. Here, we applied a mechanistic global ecosystem model to investigate the influence of large‐carnivore removal from undisturbed ecosystems. First, we simulated large‐carnivore removal on the global scale to inspect the geographic pattern of top–down control and to disentangle the functional role of large carnivores in top–down control in different environmental contexts. Second, we conducted four small‐scale ecosystem simulation experiments to understand direct and indirect changes in food‐web structure under different environmental conditions. We found that the removal of top–down control exerted by large carnivores (> 21 kg) can trigger large trophic cascades, leading to an overall decrease in autotroph biomass globally. Furthermore, the loss of large carnivores resulted in an increase of mesopredators. The magnitude of these changes was positively related to primary productivity (NPP), in line with the ‘exploitation ecosystem hypothesis’. In addition, we found that seasonality in NPP dampened the magnitude of change following the removal of large carnivores. Our results reinforce the idea that large carnivores play a fundamental role in shaping ecosystems, and further declines and extinctions can trigger substantial ecosystem responses. Our findings also support previous studies suggesting that natural ecosystem dynamics have been severely modified and are still changing as a result of the widespread decline and extinction of large carnivores.

Biogeography of eukaryotic plankton communities along the upper Yangtze River: The potential impact of cascade dams and reservoirs

The effects of large cascade dams on eukaryotic plankton communities in freshwater river-reservoir ecosystems are probably uncharted in previous studies. The purpose of this research was to investigate the large-scale genetic diversity and biogeography patterns within large cascade dams and to reveal the key drivers shaping eukaryotic plankton community. Eukaryotic plankton communities of 24 sampling sites along the upper Yangtze River were assessed based on Illumina Miseq sequencing. Generally, the mainly dominant eukaryotic plankton communities were Cryptophyta, Fungi and Alveolata, with these three clades encompassing 52.36%, 24.34% and 15.26% of all sequences. Generally, the Shannon diversity was higher in the dry season than that in the flood season, varying from 0.60 to 3.83 in the flood season and 1.41–4.53 in the dry season. Moreover, the α-diversity index decreased significantly from the upstream to the downstream. Principal coordinate analysis of the eukaryotic plankton taxa yielded a distinct biogeographical distribution pattern between different seasons and locations. Redundancy analysis results suggested that total organic carbon, DO, Chl a and PC have significant influence on the community compositions. Variation partitioning analysis indicated that the purely environmental variables, pure spatial variables, pure hydrological variables explained 17.5%, 5.1% and 10.0% of the community. The distance decay analysis (eukaryotic plankton community dissimilarity and site location) also suggests that geographical distance was important in structuring the eukaryotic plankton community and determining the spatial dissimilarity in the river network. Our findings show that eukaryotic plankton community across the upper Yangtze River exhibit an ecological succession along the river continuum, and this is primarily driven by the combination of environmental, spatial and hydrological variables in the river-reservoir system.

Optimal sizing of the grid-connected hybrid system integrating hydropower, photovoltaic, and wind considering cascade reservoir connection and photovoltaic-wind complementarity

Determining the optimal capacity is an urgent problem in the planning and construction stages of hybrid systems. This study focused on exploring a universal method for determining the capacity configuration for the grid-connected integrated system incorporating cascade hydropower, solar/photovoltaic (PV), and wind considering cascade reservoir connection and PV-wind complementarity. First, the wind and solar energy complementarity considering the resources characteristics, and the method of calculating their capacity proportions with the objective of minimizing cumulative fluctuations were proposed. Then, a large-scale hydro-PV-wind hybrid system sizing model and method considering the electricity transmission requirements and cascade hydropower comprehensive characteristics were proposed, and an optimal capacity decision method based on complementary guarantee rate (CGR) and cumulative time proportion (CTP) was formed. Finally, a case study on the upper reaches of the Yellow River in the Qinghai province of China was used to confirm their feasibility and universality. The results indicated that (1) as the PV capacity proportion increased, the cumulative fluctuations of the total output of PV and wind tended to decrease first and then increase, and the optimum capacity proportion of PV and wind for the multi-energy system was 0.744 and 0.256, respectively; (2) the optimal PV-wind capacity configurations for the world’s largest cascade hydro-PV-wind system under three- and five-segment line transmission modes were 561.2 and 651.8 MW, respectively, and the relationship between CGR, CTP, and wind-PV capacity was quantified; and (3) cascade reservoirs storage performance and solar/wind resources characteristics had a significant impact on energy complementation. Through the coordination of hydraulic and electric power between multiple stations, cascade hydropower can better complement PV/wind. However, the hydro-PV-wind combination had a negative impact on the hydropower daily power generation and the reservoir water level stability. Furthermore, for cascade hydropower, wind-solar configuration capacity should be considered mainly from the perspective of reservoir regulation performance, hydropower installed capacity, and hydropower role allocation. Thus, this sizing method provides new concepts for the large-scale development and application of clean renewable energy sources.

Induction of a local muscular dystrophy using electroporation in vivo: an easy tool for screening therapeutics

Intramuscular injection and electroporation of naked plasmid DNA (IMEP) has emerged as a potential alternative to viral vector injection for transgene expression into skeletal muscles. In this study, IMEP was used to express the DUX4 gene into mouse tibialis anterior muscle. DUX4 is normally expressed in germ cells and early embryo, and silenced in adult muscle cells where its pathological reactivation leads to Facioscapulohumeral muscular dystrophy. DUX4 encodes a potent transcription factor causing a large deregulation cascade. Its high toxicity but sporadic expression constitutes major issues for testing emerging therapeutics. The IMEP method appeared as a convenient technique to locally express DUX4 in mouse muscles. Histological analyses revealed well delineated muscle lesions 1-week after DUX4 IMEP. We have therefore developed a convenient outcome measure by quantification of the damaged muscle area using color thresholding. This method was used to characterize lesion distribution and to assess plasmid recirculation and dose–response. DUX4 expression and activity were confirmed at the mRNA and protein levels and through a quantification of target gene expression. Finally, this study gives a proof of concept of IMEP model usefulness for the rapid screening of therapeutic strategies, as demonstrated using antisense oligonucleotides against DUX4 mRNA.

Risk of Cascading Blackouts Given Correlated Component Outages

Cascading blackouts typically occur when nearly simultaneous outages occur in $k$ out of $N$ components in a power system, triggering subsequent failures that propagate through the network and cause significant load shedding. While large cascades are rare, their impact can be catastrophic, so quantifying their risk is important for grid planning and operation. A common assumption in previous approaches to quantifying such risk is that the $k$ initiating component outages are statistically independent events. However, when triggered by a common exogenous cause, initiating outages may actually be correlated. Here, copula analysis is used to quantify the impact of correlation of initiating outages on the risk of cascading failure. The method is demonstrated on two test cases; a 2383-bus model of the Polish grid under varying load conditions and a synthetic 10 000-bus model based on the geography of the Western US. The large size of the Western US test case required development of new approaches for bounding an estimate of the total number of $N-3$ blackout-causing contingencies. The results suggest that both risk of cascading failure, and the relative contribution of higher order contingencies, increase as a function of spatial correlation in component failures.

Decadal link between longitudinal morphological changes in branching channels of Yangtze estuary and movement of the offshore depo‐center

AbstractIn estuaries, the morphology of inland and offshore areas usually evolves synergistically. This study examines the decadal link between longitudinal changes in morphology of branching channels and movement of the offshore depo‐center (where sediment deposition rate is maximum) of the Yangtze River estuary, under intense human interference. Integrated data analysis is provided on morphology, runoff discharge, and ebb partition ratio from 1950 to 2017. Channel‐volume reductions and change rates between isobaths in branching channels reflect the impact of estuarine engineering projects. Ebb partition ratio and duration of discharge ≥ 60 000 m3 s‐1 act as proxies for the water excavating force in branching channels and runoff intensity. It is found that deposition occurs in the lower/upper sub‐reaches (or further downstream/upstream channels) of the inland north/south branching channels, and the offshore depo‐center moves southward or southeastward, as runoff intensity grows; the reverse occurs as runoff intensity declines. This is because the horizontal circumfluence in the Yangtze estuary rotates clockwise as ebb partition ratios of the north/south branching channels increase/decrease for increasing runoff, and conversely rotates anticlockwise for decreasing runoff. Land reclamation activities, the Deepwater Channel Project, and the Qingcaosha Reservoir have impacted greatly on longitudinal changes of morphology in the North Branch and the South Passage and on ebb partition ratio variations in the North/South Channel and the North/South Passage. Dam‐induced runoff flattening has enhanced deposition in the upper/lower sub‐reaches of the north/south branching channels and caused northward movement of the offshore depo‐center, except in areas affected by estuarine engineering projects. Dam‐induced longitudinal evolution of branching channel morphology and offshore depo‐center movement will likely persist in the future, given the ongoing construction of large cascade dams in the upper Yangtze and the completion of major projects in the Yangtze estuary. © 2020 John Wiley & Sons, Ltd.

Erosion-deposition patterns and depo-center movements in branching channels at the near-estuary reach of the Yangtze River

Channel evolution and depo-center migrations in braided reaches are significantly influenced by variations in runoff. This study examines the effect of runoff variations on the erosion-deposition patterns and depocenter movements within branching channels of the near-estuary reach of the Yangtze River. We assume that variations in annual mean duration days of runoff discharges, ebb partition ratios in branching channels, and the erosional/depositional rates of entire channels and sub-reaches are representative of variations in runoff intensity, flow dynamics in branching channels, and morphological features in the channels. Our results show that the north region of Fujiangsha Waterway, the Liuhaisha branch of Rugaosha Waterway, the west branch of Tongzhousha Waterway, and the west branch of Langshansha Waterway experience deposition or reduced erosion under low runoff intensity, and erosion or reduced deposition under high runoff intensity, with the depocenters moving upstream and downstream, respectively. Other waterway branches undergo opposite trends in erosion-deposition patterns and depo-center movements as the runoff changes. These morphological changes may be associated with trends in ebb partition ratio as the runoff discharge rises and falls. By flattening the intra-annual distribution of runoff discharge, dam construction in the Yangtze Basin has altered the ebb partition ratios in waterway branches, affecting their erosion-deposition patterns and depo-center movements. Present trends are likely to continue into the future due to the succession of large cascade dams under construction along the upper Yangtze and ongoing climate change.

Inland water's trophic status classification based on machine learning and remote sensing data

In this work, we tested machine learning algorithms in classifying waters in a reservoir cascade with basis in trophic state. The classification was done through remote sensing reflectance (Rrs) measurements collected in situ. Chlorophyll-a (chla) content determined in the laboratory were used to define the trophic state in the sampling points distributed in four reservoirs (Barra Bonita, Bariri, Ibitinga and Nova Avanhandava), located at the Tietê River, Brazil. Those four impoundments exhibit widely differing optical properties from each other, which is rather evident in relation to chla concentration. From the dataset collected in the reservoir cascade, a trophic gradient is observed, decreasing from up-to downstream. To classify the trophic state, we tested three machine learning algorithms: Artificial Neural Network (ANN), Random Forest (RF) and Support Vector Machine (SVM). Results showed that ANN and RF algorithms exhibited the best performance in classifying the different trophic state in the cascade of reservoirs. Both approaches raised a global accuracy of 80.00% and average area under Receiver Operating Characteristics (ROC) curve (AUCROC) of 0.928 and 0.912, respectively. Comparing the machine learning approaches with a parametric algorithm, only SVM presented a slightly lower performance. The outcomes of this classification can be useful for trophic state mapping considering the large cascade of reservoirs or rivers. In addition, it can give a direction in bio-optical modeling studies, which have shown that a unique bio-optical algorithm is unable to accurately retrieving concentrations of optically active constituents in aquatic system with high optical variability. So that, it is possible to develop specific chla prediction models considering the optical characteristics of each stretch of river, since machine learning-based classifications (ANN and RF) indicate different optical regions.

Turbulent drag reduction in magnetohydrodynamic and quasi-static magnetohydrodynamic turbulence

In hydrodynamic turbulence, the kinetic energy injected at large scales cascades to the inertial range, leading to a constant kinetic energy flux. In contrast, in magnetohydrodynamic (MHD) turbulence, a fraction of kinetic energy is transferred to the magnetic energy. Consequently, for the same kinetic energy injection rate, the kinetic energy flux in MHD turbulence is reduced compared to its hydrodynamic counterpart. This leads to relative weakening of the nonlinear term ⟨|(u·∇)u|⟩, (where u is the velocity field) and turbulent drag, but strengthening of the velocity field in MHD turbulence. We verify the above using shell model simulations of hydrodynamic and MHD turbulence. Quasi-static MHD turbulence too exhibits turbulent drag reduction similar to MHD turbulence.

Improving the Robustness of Online Social Networks: A Simulation Approach of Network Interventions.

Online social networks (OSN) are prime examples of socio-technical systems in which individuals interact via a technical platform. OSN are very volatile because users enter and exit and frequently change their interactions. This makes the robustness of such systems difficult to measure and to control. To quantify robustness, we propose a coreness value obtained from the directed interaction network. We study the emergence of large drop-out cascades of users leaving the OSN by means of an agent-based model. For agents, we define a utility function that depends on their relative reputation and their costs for interactions. The decision of agents to leave the OSN depends on this utility. Our aim is to prevent drop-out cascades by influencing specific agents with low utility. We identify strategies to control agents in the core and the periphery of the OSN such that drop-out cascades are significantly reduced, and the robustness of the OSN is increased.

The Inadequacy of Capital Adequacy: Bank Failures in Complex Systems

I provide simulation evidence that the complex systems framework is well-suited for explaining the historical distribution of U.S. commercial bank failure cascades. I use the complex systems framework to test a model of the efficacy of microprudential (bank-level) ratio-based capital adequacy regulations. I find that these requirements generally have the adverse effect of increasing the likelihood of large failure cascades. This adverse effect is especially pronounced under simulation conditions that mimic economic downturns. If banks use more leverage in response to decreased capital adequacy requirements, the likelihood of failure cascades increases only minimally. These results suggest that existing microprudential capital adequacy requirements might be counterproductive to the goal of mitigating bank failure cascades, and provide theoretical support for a countercyclical capital buffer.

Crosstalk in the darkness: bulb vernalization activates meristem transition via circadian rhythm and photoperiodic pathway

BackgroundGeophytes possess specialized storage organs - bulbs, tubers, corms or rhizomes, which allow their survival during unfovarable periods and provide energy support for sprouting and sexual and vegetative reproduction. Bulbing and flowering of the geophyte depend on the combined effects of the internal and external factors, especially temperature and photoperiod. Many geophytes are extensively used in agriculture, but mechanisms of regulation of their flowering and bulbing are still unclear.ResultsComparative morpho-physiological and transcriptome analyses and quantitative validation of gene expression shed light on the molecular regulation of the responses to vernalization in garlic, a typical bulbous plant. Long dark cold exposure of bulbs is a major cue for flowering and bulbing, and its interactions with the genetic makeup of the individual plant dictate the phenotypic expression during growth stage. Photoperiod signal is not involved in the initial nuclear and metabolic processes, but might play role in the later stages of development, flower stem elongation and bulbing. Vernalization for 12 weeks at 4 °C and planting in November resulted in flower initiation under short photoperiod in December–January, and early blooming and bulbing. In contrast, non-vernalized plants did not undergo meristem transition. Comparisons between vernalized and non-vernalized bulbs revealed ~ 14,000 differentially expressed genes.ConclusionsLow temperatures stimulate a large cascades of molecular mechanisms in garlic, and a variety of flowering pathways operate together for the benefit of meristem transition, annual life cycle and viable reproduction results.The circadian clock appears to play a central role in the transition of the meristem from vegetative to reproductive stage in bulbous plant, serving as integrator of the low-temperature signals and the expression of the genes associated with vernalization, photoperiod and meristem transition. The reserved photoperiodic pathway is integrated at an upstream point, possibly by the same receptors. Therefore, in bulb, low temperatures stimulate cascades of developmental mechanisms, and several genetic flowering pathways intermix to achieve successful sexual and vegetative reproduction.

A Markovian Influence Graph Formed From Utility Line Outage Data to Mitigate Large Cascades

We use observed transmission line outage data to make a Markovian influence graph that describes the probabili- ties of transitions between generations of cascading line outages. Each generation of a cascade consists of a single line outage or multiple line outages. The new influence graph defines a Markov chain and generalizes previous influence graphs by including multiple line outages as Markov chain states. The generalized influence graph can reproduce the distribution of cascade size in the utility data. In particular, it can estimate the probabilities of small, medium and large cascades. The influence graph has the key advantage of allowing the effect of mitigations to be analyzed and readily tested, which is not available from the observed data. We exploit the asymptotic properties of the Markov chain to find the lines most involved in large cascades and show how upgrades to these critical lines can reduce the probability of large cascades.

Climate Change Impacts on Hydropower in Yunnan, China

Climate change could have dire effects on hydropower systems, especially in southwest China, where hydropower dominates the regional power system. This study examines two large cascade hydropower systems in Yunnan province in southwest China for 10 climate change projections made with 5 global climate models (GCMs) and 2 representative concentration pathways (RCPs) under Coupled Model Intercomparison Project Phase 5 (CMIP5). First, a back propagation neural network rain-runoff model is built for each hydropower station to estimate inflows with climate change. Then, a progressive optimality algorithm maximizes hydropower generation for each projection. The results show generation increasing in each GCM projection, but increasing more in GCMs under scenario RCP8.5. However, yearly generation fluctuates more: generation decreases dramatically with potential for electricity shortages in dry years and more electricity as well as spill during wet years. Average annual spill, average annual inflow and average storage have similar trends. The analysis indicates that a planned large dam on the upper Jinsha River would increase seasonal regulation ability, increase hydropower generation, and decrease spill. Increased turbine capacity increases generation slightly and decreases spill for the Lancang River. Results from this study demonstrate effects of climate change on hydropower systems and identify which watersheds might be more vulnerable, along with some actions that could help adapt to climate change.

Diffusion Games

Behaviors and information often spread via person-to-person diffusion. This paper highlights how diffusion processes can facilitate coordination. I study contagion in a discrete network with Bayesian players. In addition to characterizing the extent and rate of adoption, we uncover a new effect: when large cascades are possible in equilibrium, exposure conveys information about a player’s network position. This effect underscores a novel trade-off in the design of marketing campaigns, suggesting conditions under which word-of-mouth is relatively more effective. A generalization of the model to multi-type networks suggests a new approach to targeted seeding. (JEL D83, D85, M31, M37, Z13)

Is Peer Influence Essential for Success?

A general conjecture is that successful products attain their popularity through influence of adopters on their peers and product information disseminating over the social network. Indeed, many studies have confirmed the existence of local peer effects and contagion. But others have shown that peer influence has a marginal, if any, effect on cascades of adoptions. In this work, we study this discrepancy by analyzing video games propagating over the social network of gamers on Steam, the world’s largest video game platform. A major identification problem – distinguishing homophily from peer influence – is a challenge in any peer influence study based on observational data. To overcome it, we introduce a novel method, Revealed Preference-based Matching Estimation, that estimates the impact of peer influence on adoption by using unsupervised machine-learning algorithm to match product adopters to users based solely on similarity of their past adoption. This procedure is applied to thousands of products and reveals how peer influence changes over their lifecycle, thus allowing us to draw general conclusions about the entire ecosystem. Results show that most products belong to one of two distinct groups, each exhibiting a characteristic temporal pattern of adoption: products that exhibit substantial peer influence; and products that do not, for which adoption is driven by preferences. Considering the reach of products in each group, surprisingly, we found that local peer effects are stronger in less popular products. Even more surprising is the fact that almost all blockbusters (products adopted by millions of users) did not exhibit substantial peer influence at any stage of their lifecycle. These results shed light on the discrepancy between observed local peer effects and the lack of peer influence in large adoption cascades that are characteristic of successful products.

Research progress of hydrogen/helium effects in metal materials by positron annihilation spectroscopy

An important feature of the irradiation process in nuclear system is the formation of large displacement cascades, in which primary knock-on atoms and secondary particles formed by nuclear reactions generate a considerable number of defects such as dislocations, vacancies and transmutation gases. Predicting and mitigating the adverse effects of damage defect and transmutation hydrogen/helium produced by high-dose neutron irradiation on the mechanical properties of structural materials is the most significant challenge facing the current development of nuclear energy. To solve this problem, understanding the interaction mechanism between hydrogen/helium atoms and micro-defects is a very important breakthrough. Precursors of helium/ hydrogen bubble, small helium/hydrogen-filled vacancy complexes, may play an important role in realizing bubble nucleation, and the formation of these complexes is affected by many factors. However, only a little information about helium/hydrogen-vacancy clusters’ behavior has been obtained in metal/alloy materials. This is mainly limited by the characterization methods, such as the limited resolution of transmission electron microscope (TEM). Helium/hydrogen-vacancy clusters cannot be observed by TEM before the formation of helium bubbles. Applications of positron annihilation to the study of crystal lattice defects started around 1970s, when it was realized that positron annihilation is particularly sensitive to vacancy-type defects and that annihilation properties manifest the nature of each specific type of defect. In recent years, with the continuous development of slow positron beam and the improvement of various experimental testing methods based on slow positron beam, the application of positron annihilation technology has been extended to the research field of hydrogen/helium behavior in metal materials, which plays an important role in studying the hydrogen/helium radiation damage to metal materials. In this review, the basic principles of positron annihilation spectroscopy are briefly discussed and the three most important measurement methods used for hydrogen/helium effect studies are described (i.e. positron annihilation lifetime spectroscopy (PALS), Doppler broadening spectroscopy (DBS), coincidence Doppler broadening spectroscopy (CDBS)). In this paper, the application of positron annihilation spectroscopy to the study of hydrogen/helium behavior in metal materials is reviewed in combination with the reported relevant developments (including our research group’s achieve-ments). The advantages of three commonly used measurement methods in the following specific studies are highlighted: 1) The estimation of bubble size and concentration; 2) irradiation damage induced by hydrogen/helium; 3) the evolution behavior of irradiation-induced defects in the heat treatment process; 4) sy-nergistic effect of hydrogen and helium.