Distinct Blocks Research Articles

Reconstructing the Uncertain Past: Cracked Docudramas That Question Their Own Authority

Reconstructing the Uncertain Past: Cracked Docudramas That Question Their Own Authority

Nonstationary seasonal model for daily mean temperature distribution bridging bulk and tails

In traditional extreme value analysis, the bulk of the data is ignored, and only the tails of the distribution are used for inference. Extreme observations are specified as values that exceed a threshold or as maximum values over distinct blocks of time, and subsequent estimation procedures are motivated by asymptotic theory for extremes of random processes. For environmental data, nonstationary behavior in the bulk of the distribution, such as seasonality or climate change, will also be observed in the tails. To accurately model such nonstationarity, it seems natural to use the entire dataset rather than just the most extreme values. It is also common to observe different types of nonstationarity in each tail of a distribution. Most work on extremes only focuses on one tail of a distribution, but for temperature, both tails are of interest. This paper builds on a recently proposed parametric model for the entire probability distribution that has flexible behavior in both tails. We apply an extension of this model to historical records of daily mean temperature at several locations across the United States with different climates and local conditions. We highlight the ability of the method to quantify changes in the bulk and tails across the year over the past decades and under different geographic and climatic conditions. The proposed model shows good performance when compared to several benchmark models that are typically used in extreme value analysis of temperature.

Exploring the Spatial Correlation Network Structure of Green Innovation Efficiency in the Yangtze River Delta, China

Green innovation is crucial to high-quality economic development and has become an important engine for green transformation development in the Yangtze River Delta region. In this study, we used the super epsilon-based measure (super-EBM) to measure the green innovation efficiency of 26 cities in the Yangtze River Delta region of China from 2003 to 2018. Secondly, on the basis of a modified gravity model, we determined the spatial correlation of the Yangtze River Delta region’s green innovation efficiency and built a relation matrix. Finally, using the Moran index, centrality analysis, and a block model, we investigated its spatial characteristics and empirically analyzed its influencing factors by applying the quadratic assignment procedure. The results show that (1) in spatial terms, the Yangtze River Delta’s green innovation efficiency is extremely unbalanced, and the spatial network association density is low, only 0.218; (2) in terms of block analysis, the green innovation efficiency of the Yangtze River Delta region’s cities can be divided into four blocks, with distinct blocks holding distinct responsibilities; and (3) in terms of influencing factors, geographic distance; the expansion of the difference in energy consumption and the environment pollution index; and narrowing the gap in economic development, the industrial structure, and green coverage will push forward the formation of spatial correlation at a significance level of 10%. Our findings expand the research on traditional innovation efficiency and provide theoretical guidance for formulating regional green innovation coordinated development policy. It is necessary to strengthen urban green innovation cooperation in the Yangtze River Delta and promote regional integrated development. Different policies should be adopted for cities with different spatial correlation patterns. From the perspective of block analysis, it is necessary to balance the acceptance relationship and spillover relationship between cities. In addition, to improve the spatial correlation of green innovation efficiency in the Yangtze River Delta, the allocation of green innovation resources in the Yangtze River Delta should be optimized.

Geophysical data reveal a tectonic indentation and constrain the transition zone between the São Francisco Craton and the Borborema Paleoplates across the Neoproterozoic Sergipano Orogen, beneath the Cretaceous Tucano Basin, NE Brazil

Geophysical techniques can improve knowledge about the deep structures of collisional orogens. Therefore, magnetotelluric and gravimetric data were combined in this study to enhance the poorly understood subsurface structure of the transition zone from the São Francisco Craton to the Neoproterozoic Sergipano Orogen in northeastern Brazil. The results along a 240-km-long, NE–SW trending geophysical transect revealed two main geoelectric domains beneath the Cretaceous Tucano Basin, which can be linked to two distinct tectonic blocks on the surface. The less resistive SW domain corresponds to the rocks of the Serrinha Block within the São Francisco Craton. In contrast, the more resistive NE domain matches the basement rocks of the northernmost units of the Sergipano Orogen. The results are discussed considering Paleoproterozoic and Neoproterozoic scenarios with different interpretations of the NE domain. Based on the surface geology and additional gravimetric and magnetotelluric data, the best scenario is to consider the NE domain as representing the geoelectric signature of the three northernmost geological domains of the Sergipano Orogen (Macururé, Poço Redondo–Marancó, and Canindé) and that of the Pernambuco–Alagoas Block (PEAL). PEAL collided with the São Francisco paleolithosphere forming the Sergipano Orogen. The NE geoelectric domain can be interpreted as an indenter of the tectonically intruded PEAL beneath the São Francisco paleolithosphere. The new data properly define the northern limit of the São Francisco Craton and explain both indentation structures and contrasting sediment provenance observed in the Sergipano Orogen.

Fluctuations of the Magnetization in the Block Potts Model

In this note we study the block spin mean-field Potts model, in which the spins are divided into s blocks and can take qge 2 different values (colors). Each block is allowed to contain a different proportion of vertices and behaves itself like a mean-field Ising/Potts model which also interacts with other blocks according to different temperatures. Of particular interest is the behavior of the magnetization, which counts the number of colors appearing in the distinct blocks. We prove central limit theorems for the magnetization in the generalized high-temperature regime and provide a moderate deviation principle for its fluctuations on lower scalings. More precisely, the magnetization concentrates around the uniform vector of all colors with an explicit, but singular, Gaussian distribution. In order to remove the singular component, we will also consider a rotated magnetization, which enables us to compare our results to various related models.

Enhanced Convolutional Neural Network Model for Cassava Leaf Disease Identification and Classification

Cassava is a crucial food and nutrition security crop cultivated by small-scale farmers and it can survive in a brutal environment. It is a significant source of carbohydrates in African countries. Sometimes, Cassava crops can be infected by leaf diseases, affecting the overall production and reducing farmers’ income. The existing Cassava disease research encounters several challenges, such as poor detection rate, higher processing time, and poor accuracy. This research provides a comprehensive learning strategy for real-time Cassava leaf disease identification based on enhanced CNN models (ECNN). The existing Standard CNN model utilizes extensive data processing features, increasing the computational overhead. A depth-wise separable convolution layer is utilized to resolve CNN issues in the proposed ECNN model. This feature minimizes the feature count and computational overhead. The proposed ECNN model utilizes a distinct block processing feature to process the imbalanced images. To resolve the color segregation issue, the proposed ECNN model uses a Gamma correction feature. To decrease the variable selection process and increase the computational efficiency, the proposed ECNN model uses global average election polling with batch normalization. An experimental analysis is performed over an online Cassava image dataset containing 6256 images of Cassava leaves with five disease classes. The dataset classes are as follows: class 0: “Cassava Bacterial Blight (CBB)”; class 1: “Cassava Brown Streak Disease (CBSD)”; class 2: “Cassava Green Mottle (CGM)”; class 3: “Cassava Mosaic Disease (CMD)”; and class 4: “Healthy”. Various performance measuring parameters, i.e., precision, recall, measure, and accuracy, are calculated for existing Standard CNN and the proposed ECNN model. The proposed ECNN classifier significantly outperforms and achieves 99.3% accuracy for the balanced dataset. The test findings prove that applying a balanced database of images improves classification performance.

Supramolecular Peptide Nanofiber Hydrogels for Bone Tissue Engineering: From Multihierarchical Fabrications to Comprehensive Applications.

Bone tissue engineering is becoming an ideal strategy to replace autologous bone grafts for surgical bone repair, but the multihierarchical complexity of natural bone is still difficult to emulate due to the lack of suitable biomaterials. Supramolecular peptide nanofiber hydrogels (SPNHs) are emerging biomaterials because of their inherent biocompatibility, satisfied biodegradability, high purity, facile functionalization, and tunable mechanical properties. This review initially focuses on the multihierarchical fabrications by SPNHs to emulate natural bony extracellular matrix. Structurally, supramolecular peptides based on distinctive building blocks can assemble into nanofiber hydrogels, which can be used as nanomorphology‐mimetic scaffolds for tissue engineering. Biochemically, bioactive motifs and bioactive factors can be covalently tethered or physically absorbed to SPNHs to endow various functions depending on physiological and pharmacological requirements. Mechanically, four strategies are summarized to optimize the biophysical microenvironment of SPNHs for bone regeneration. Furthermore, comprehensive applications about SPNHs for bone tissue engineering are reviewed. The biomaterials can be directly used in the form of injectable hydrogels or composite nanoscaffolds, or they can be used to construct engineered bone grafts by bioprinting or bioreactors. Finally, continuing challenges and outlook are discussed.

Strategies for handling missing data that improve Frailty Index estimation and predictive power: lessons from the NHANES dataset.

Missing data are ubiquitous in aging studies. Combining the National Health and Nutrition Examination Survey (NHANES) 2003/2004 and 2005/2006 cross-sectional aging studies (N = 9307), we investigated the effects of both real and simulated missing data on the Frailty Index (FI) and survival analysis, along with several mitigation strategies. We observed distinct block patterns of missing variables in the dataset. These blocks showed significant hazard rate (HR) differences when they were missing versus present, indicating that missingness cannot be simply ignored. Simulations of this patterned missingness produced a bias of 0.0112 ± 0.0008 to the mean FI when missing values were ignored, representing a change in hazard of 1.09 ± 0.01. A similar bias of 0.0106 ± 0.0001 was estimated in the real missingness. Imputation was able to correct the bias using the multivariate imputation by chained equations (MICE) method via the classification and regression tree (CART) prediction model together with rule-based imputation. Using auxiliary variables (CART+Aux) improved the performance of CART. Well-performing imputation models, especially CART+Aux, were able to increase the FI predictive power and the reliability of the HR estimates. In contrast, the default MICE models, predictive mean matching/logistic regression (PMM/logreg), caused even stronger biases to the FI. Our results demonstrate that calibration of the FI as a mortality predictor depends on how missing data are handled. Ignoring missing values when calculating the FI may be an acceptable strategy for clinical settings where the FI is used as a rough predictor of adverse outcomes. Where the FI is to be compared across studies or populations, judicious imputation - cognizant of the risks carried by poor imputation - should be used to ensure reliability and precision of statistical estimates and conclusions.

DEVELOPMENT OF THE QUESTIONNAIRE AND ESTIMATION OF THE PARTICULARITIES OF OWN EXHAUSTION IN MEDICAL WORKERS UNDER COVID-19 PANDEMIC CONDITIONS. PILOT STUDY

Burnout syndrome is defined in the ICD-11. stated in section "273 – Problems related to the difficulty of organizing their own way of life", is a real scourge of the XXI century, especially for people who work within a communication-related field. This problem has gained particular importance under the influence of the coronavirus pandemic. However, the issues on development and specific manifestations of occupational stress among doctors of different specialties have not been systematically studied yet. Specialty scientific papers on the indicators that reflect the stress and burnout were consulted for the elaboration of the working tool (IPSSHM questionnaire - for diagnosing the professional stress syndrome in the healthcare workers) and selecting the testing methodology. A pilot study was conducted to assess the burnout syndrome as a complex systemic state of the professional activity of various medical specialists in the Republic of Moldova, depending on their psychophysiological characteristics. A questionnaire, which includes two parts (general and special), was developed. The general part of the questionnaire (with ten items) allowed to collect personal social, professional, and married data about the respondent, regarding age, biological gender, family situation, living environment, activity environment, medical specialty, place/work institution, work experience, and information about a part-time job. The unique part of the questionnaire represents a set of 131 items, which characterize the respondent through the prism of six distinctive structural blocks: emotional-sentimental, behavioral block, cognitive block, communicative block, individual-semantic block, and neurophysiological block. 38.3% of themedical workers tested (n=73) did not suffer from burnout, while 61.6% of them presented various signs of burnout. The specific characteristics of the professional exhaustion among healthcare workers were revealed depending on their specialty and work experience, as well as on their psychophysiological peculiarities. The most susceptible to stress factors were the healthcare workers with <10 years of working experience (77.1%). The constituent mental health blocks showed changes among different medical specialties under the influence of occupational exhaustion.The obtained results demonstrate the validity of the questionnaire and the possibility of its application identifying healthcare workers' burnout. A relationship between physician burnout, their behavioral type and level of emotional stability has been demonstrated.

Origin of ultramafic–mafic bodies on the Isles of Lewis and Harris (Scotland, UK): Constraints on the Archean–Paleoproterozoic evolution of the Lewisian Gneiss Complex, North Atlantic Craton

The Lewisian Gneiss Complex (LGC) is a tonalite-trondhjemite-granodiorite (TTG)-dominated fragment of the North Atlantic Craton (NAC) in northwest Scotland. End-member models describe the LGC as representing either a continuous piece of Archean crust or up to 12 geologically distinct Archean terranes, with interpretations sitting on a spectrum between these end-members. There is particular uncertainty over the correlations between the Archean–Paleoproterozoic magmatic and metamorphic events recorded by mainland part of the LGC and the part exposed on the Outer Hebridean islands of Lewis and Harris. In this paper, we present the results of field mapping, petrography, and major, trace and platinum-group element (PGE) bulk-rock geochemistry for four ultramafic–mafic bodies in Lewis and Harris, namely: Maaruig, Loch Mhorsgail, Coltraiseal Mor and Beinn a’ Chuailean. We consider the effects of metamorphism and element mobility, their petrogenesis, and potential correlations with ultramafic–mafic rocks elsewhere in the LGC. Our data indicate that the studied ultramafic–mafic rocks can be subdivided into two petrologically distinct groups. Metaperidotites and metapyroxenites from Maaruig and Loch Mhorsgail are interpreted as Archean (>2.8 Ga) cumulates distinct from anything currently identified in the mainland LGC, with this interpretation based on distinctive modal layering, a discordance with surrounding TTG gneiss, fractionated PGE patterns ([Pd/Ir]N = 1.3–6.6) and negative HFSE anomalies ([Nb/La]N = 0.2–0.8). Metagabbronorites from Coltraiseal Mor and Beinn a’ Chuailean, which also exhibit negative high field strength-element (HFSE) anomalies ([Nb/La]N = 0.2–0.7) and show mildly fractionated ([Pd/Ir]N = 1.2–2.8) PGE patterns, most likely represent deformed Paleoproterozoic dykes. These occurrences could be correlatives of a suite of ca. 2.4 Ga mafic dykes exposed throughout the mainland LGC (the Scourie Dykes), with the Outer Hebridean occurrences having experienced more intense Paleoproterozoic (Laxfordian) deformation/reworking. These interpretations suggest that the LGC lithologies of Lewis and Harris were proximal to the mainland LGC’s Central Region by the early Paleoproterozoic but raises the possibility that they were distinct crustal blocks in the Mesoarchean.

Phonetic Segmentation using a Wavelet-based Speech Cepstral Features and Sparse Representation Classifier

Speech segmentation is the process of dividing speech signal into distinct acoustic blocks that could be words, syllables or phonemes. Phonetic segmentation is about finding the exact boundaries for the different phonemes that composes a specific speech signal. This problem is crucial for many applications, i.e. automatic speech recognition (ASR). In this paper we propose a new model-based text independent phonetic segmentation method based on wavelet packet speech parametrization features and using the sparse representation classifier (SRC). Experiments were performed on two datasets, the first is an English one derived from TIMIT corpus, while the second is an Arabic one derived from the Arabic speech corpus. Results showed that the proposed wavelet packet de composition features outperform the MFCC features in speech segmentation task, in terms of both F1-score and R-measure on both datasets. Results also indicate that the SRC gives higher hit rate than the famous k-Nearest Neighbors (k-NN) classifier on TIMIT dataset.

Blending Linear and Cyclic Block Copolymers to Manipulate Nanolithographic Feature Dimensions.

Block copolymers (BCPs) consist of two or more covalently bound chemically distinct homopolymer blocks. These macromolecules have emerging applications in photonics, membrane separations, and nanolithography stemming from their self-assembly into regular nanoscale structures. Theory suggests that cyclic BCPs should form features up to 40% smaller than their linear analogs while also exhibiting superior thin-film stability and assembly dynamics. However, the complex syntheses required to produce cyclic polymers mean that a need for pure cyclic BCPs would present a challenge to large-scale manufacturing. Here, we employ dissipative particle dynamics simulations to probe the self-assembly behavior of cyclic/linear BCP blends, focusing on nanofeature size and interfacial width as these qualities are critical to nanopatterning applications. We find that for mixtures of symmetric cyclic and linear polymers with equivalent lengths, up to 10% synthetic impurity has a minimal impact on cyclic BCP feature dimensions and interfacial roughness. On the other hand, blending with cyclic BCPs provides a route to “fine-tune” linear BCP feature sizes. We analyze simulated blend domain spacings within the context of strong segregation theory and find significant deviations between simulation and theory that arise from molecular-level packing motifs not included in theory. These insights into blend self-assembly will assist experimentalists in rationally designing BCP materials for advanced nanolithography applications.

Fragmentations with self-similar branching speeds

AbstractWe consider fragmentation processes with values in the space of marked partitions of $\mathbb{N}$, i.e. partitions where each block is decorated with a nonnegative real number. Assuming that the marks on distinct blocks evolve as independent positive self-similar Markov processes and determine the speed at which their blocks fragment, we get a natural generalization of the self-similar fragmentations of Bertoin (Ann. Inst. H. Poincaré Prob. Statist.38, 2002). Our main result is the characterization of these generalized fragmentation processes: a Lévy–Khinchin representation is obtained, using techniques from positive self-similar Markov processes and from classical fragmentation processes. We then give sufficient conditions for their absorption in finite time to a frozen state, and for the genealogical tree of the process to have finite total length.

Impact of Nelarabine, Intensive L-Asparaginase, and Protracted Intrathecal Therapy on Newly Diagnosed T-Cell Acute Lymphoblastic Leukemia: Results from the Japanese Pediatric Leukemia/Lymphoma Study Group and the Japan Adult Leukemia Study Group

Impact of Nelarabine, Intensive L-Asparaginase, and Protracted Intrathecal Therapy on Newly Diagnosed T-Cell Acute Lymphoblastic Leukemia: Results from the Japanese Pediatric Leukemia/Lymphoma Study Group and the Japan Adult Leukemia Study Group

Diurnal fluctuations in musical preference.

The rhythm of human life is governed by diurnal cycles, as a result of endogenous circadian processes evolved to maximize biological fitness. Even complex aspects of daily life, such as affective states, exhibit systematic diurnal patterns which in turn influence behaviour. As a result, previous research has identified population-level diurnal patterns in affective preference for music. By analysing audio features from over two billion music streaming events on Spotify, we find that the music people listen to divides into five distinct time blocks corresponding to morning, afternoon, evening, night and late night/early morning. By integrating an artificial neural network with Spotify's API, we show a general awareness of diurnal preference in playlists, which is not present to the same extent for individual tracks. Our results demonstrate how music intertwines with our daily lives and highlight how even something as individual as musical preference is influenced by underlying diurnal patterns.

The microstructure and crystallography of lath martensite with Greninger-Troiano orientation relationship in a Fe-12.8Ni-1.5Si-0.22%C steel

The excellent matching of strength and toughness with respect to lath martensite is closely related to its hierarchical configuration and crystallographic features. Recently, a few literatures reported that the Greninger-Troiano (G-T) relationship, instead of the generally accepted Kurdjumov-Sachs (K S) relation, was determined in some lath martensite steels. However, the underlying reason is still waiting to be explored. In the present study, systematic characterizations were performed in water-quenched Fe-12Ni-1.5Si-0.2C steel firstly, then a modified version of crystallographic model developed by Morris et al. was proposed to clarify the microstructure and the crystallography with G-T relationship. As the main crystallographic features, the orientation relationship between prior austenite and martensitic variants is close to G-T rather than K S relation. Further characterization shows that the block interface is parallel to the annealing twin boundary of prior austenite, indicating the {111} γ habit plane of blocks. Naturally, three crystallographically distinct blocks with {111} γ interfaces can be configured into a packet with self-accommodation manner to minimize macro strain. In fact, such a hierarchical configuration with G-T relationship can be understood clearly by the modified version of crystallographic model. Firstly, regarding the bivariant block as the basic element which undertakes the invariant plane shear, the total shear strain within each packet region can thus be effectively cancelled out on the common habit planes of {111} γ due to the self-accommodation effect, leading to an essentially shear strain-free pattern; Secondly, a higher hierarchy is accompanied with a significant decrease of strain and only a homogenous dilation of 0.02 remains in a prior austenite grain, which is closely related to the unique hierarchy; Thirdly, the G-T relationship is natural preferred for the present steel, which satisfies two conditions in this crystallographic model: (i) a minimum of angular deviation between the {111} γ and the habit plane of bivariant blocks; (ii) three slip systems of dislocations with 〈111〉 α’ Burgers vectors involved during the martensitic transformation, which is also universally observed in the TEM examinations. Besides that, compared with the case of K S relationship, a potential influence of G-T relationship on mechanical properties may embody in inducing a lower ductile-brittle transition temperature and better toughness of lath martensite steels. • G-T relation was determined by a newly-developed scheme. • Microstructure and crystallography were clarified by a modified model. • A higher hierarchy is accompanied with a significant decrease of strain.

Dissociating the Impact of Movement Time and Energy Costs on Decision-Making and Action Initiation in Humans.

Recent theories and data suggest that adapted behavior involves economic computations during which multiple trade-offs between reward value, accuracy requirement, energy expenditure, and elapsing time are solved so as to obtain rewards as soon as possible while spending the least possible amount of energy. However, the relative impact of movement energy and duration costs on perceptual decision-making and movement initiation is poorly understood. Here, we tested 31 healthy subjects on a perceptual decision-making task in which they executed reaching movements to report probabilistic choices. In distinct blocks of trials, the reaching duration (“Time” condition) and energy (“Effort” condition) costs were independently varied compared to a “Reference” block, while decision difficulty was maintained similar at the block level. Participants also performed a simple delayed-reaching (DR) task aimed at estimating movement initiation duration in each motor condition. Results in that DR task show that long duration movements extended reaction times (RTs) in most subjects, whereas energy-consuming movements led to mixed effects on RTs. In the decision task, about half of the subjects decreased their decision durations (DDs) in the Time condition, while the impact of energy on DDs were again mixed across subjects. Decision accuracy was overall similar across motor conditions. These results indicate that movement duration and, to a lesser extent, energy expenditure, idiosyncratically affect perceptual decision-making and action initiation. We propose that subjects who shortened their choices in the time-consuming condition of the decision task did so to limit a drop of reward rate.

Model-scale tests on ice-structure interaction in shallow water: Global ice loads and the ice loading process

Laboratory-scale experiments on ice-structure interaction process in shallow water were performed by pushing a ten-meter-wide ice sheet against an inclined structure of the same width. Seven experiments were performed in three series: In one of the series, the compressive and flexural strengths were both about 50kPa, in the two other test series the ice strength was two and four times higher. The ice thickness was about 50 mm in all experiments. The loading process showed two phases: the ice load on the structure (1) first increased linearly with a rate that was constant for all experiments, after which (2) the loading process reached a steady-state phase with approximately constant load. The magnitude of ice loads was not proportional to ice strength, as the weakest ice yielded higher loads than the ice having twice its strength. The ice rubble grounded in all experiments, but the bottom carried only a small portion of the load. The load records could be normalized by a factor combining the weight and the characteristic length of the intact ice. Based on the normalization, a model explaining the loading process was derived; the weight of the incoming ice has a dominant role during phase (1), while buckling explains the change in the process to phase (2) when the ice is strong enough. The loading process for the weakest ice was different from that for the other two ice types used. For example, instead of forming a rubble pile consisting of distinct ice blocks, weakest ice formed a dense pile of slush. The normalized ice load data highlighted the differences in the loading process.

Complex tectonic setting and deep crustal seismicity of the Sikkim Himalaya: An electrical resistivity perspective

Tectonic setting of the Himalayan arc is dominated by thrust-type earthquakes mainly associated with rupture of the décollement surface, also called the Main Himalayan Thrust (MHT). However, the region encompassing eastern Nepal, Sikkim and western Bhutan departs from this general tectonic model of the Himalaya and experiences deep crustal seismicity of strike-slip nature with focal planes oblique to the convergence direction of the collision front. The 2011 Sikkim earthquake (Mw 6.9) is an example of such transverse tectonic deformation. Several geophysical studies have been carried out in Sikkim to understand the complex tectonics of this region. Among these, a crustal model derived from magnetotelluric (MT) data revealed contrasting crustal structure across the Main Central Thrust Zone (MCTZ) beside a small-scale MHT ramp. Here, we revisit the MT model by including long-period MT data (up to 10,000 s) at six sites along the profile, invert the distortion corrected impedance data, and analyse the results in conjunction with other published geophysical and structural information. Based on this study, we propose a tectonic model for the Sikkim Himalaya that reveals the complex nature of the lithospheric structure including two major contacts beneath the MCTZ and the Main Frontal Thrust (MFT), respectively. The contact beneath the MCTZ is a NW-SE trending lithospheric-scale seismogenic fault that separates two geologically and compositionally distinct blocks of the Indian plate underthrusting the Himalaya. The other contact beneath the MFT could be responsible for the necking of the Main Central Thrust (MCT) at this location.

Existence of the Dharwar–Bastar–Singhbhum (DHABASI) megacraton since 3.35 Ga: constraints from the Precambrian large igneous province record

Abstract We propose a Precambrian megacraton (consisting of two or more ancient cratons), DHABASI in the Indian Shield, which includes the Dharwar, Bastar and Singhbhum cratons. This interpretation is mainly based on seven large igneous provinces (LIPs) that are identified in these three cratons over the age range of c. 3.35–1.77 Ga, a period of at least 1.6 Ga. The absence of any subsequent break-up of DHABASI since 1.77 Ga suggests that this megacraton has existed for the past 3.35 Ga. In addition to their use in recognizing this megacraton, these LIP events may also provide likely targets for Cu–Ni–Cr–Co–platinum group element deposits. We suggest that the megacraton DHABASI was an integral part of supercontinents/supercratons through Earth's history, and that it should be utilized as a distinct building block for palaeocontinental reconstructions rather than using the individual Dharwar, Bastar and Singhbhum cratons.