97 research outputs found
Signature of an antiferromagnetic metallic ground state in heavily electron-doped Sr2FeMoO6
No full textSr2FeMoO6 is a well-known double perovskite with exciting high-temperature magnetic properties. Through various magnetic and spectroscopic measurements, we collect compelling evidence here that this compound can be driven into a rare three-dimensional antiferromagnetic metallic state by heavy electron doping (70% Sr2+ substitution by La3+). Moreover, local structural study of these Sr2-xLaxFeMoO6 (1.0 <= x <= 1.5) compounds reveals unusual atomic scale phase distribution in terms of La,Fe- and Sr,Mo-rich regions driven by strong La-O covalency, a phenomenon hitherto undisclosed in double perovskites. The general trend of our findings is in agreement with theoretical calculations carried out on realistic structures having local chemical fluctuations, which reconfirms the relevance of the kinetic-energy-driven magnetic model. RI Sarkar, Soumyajit/B-1628-201
Atlas of Structural Geology /
No full textAtlas of Structural Geology features a broad and inclusive range of high-quality meso- and micro-scale full-color photographs, descriptions, and captions related to the deformation of rocks and geologic structures. It is a multi-contributed, comprehensive reference that includes submissions from many of the world's leading structural geologists, making it the most thorough and comprehensive reference available to the scientific community. All types of structures are featured, including structures related to ductile and brittle shear zones, sigma- and delta-structures, mineral fish, duplexe.Includes bibliographical references and indexes.Vendor-supplied metadata.Front Cover; Atlas of Structural Geology; Copyright; Contents; List of Contributors; Preface; Acknowledgments; Chapter 1 -- Folds; REFERENCES; Chapter 2 -- Ductile Shear Zones; REFERENCES; Chapter 3 -- Brittle Faults; REFERENCES; Chapter 4 -- Boudins and Mullions; REFERENCES; Chapter 5 -- Veins; REFERENCES; Chapter 6 -- Various Structures; REFERENCES; Author Index; Subject Index.Atlas of Structural Geology features a broad and inclusive range of high-quality meso- and micro-scale full-color photographs, descriptions, and captions related to the deformation of rocks and geologic structures. It is a multi-contributed, comprehensive reference that includes submissions from many of the world's leading structural geologists, making it the most thorough and comprehensive reference available to the scientific community. All types of structures are featured, including structures related to ductile and brittle shear zones, sigma- and delta-structures, mineral fish, duplexe.Elsevie
Characterization of the high frequency alternating current block in the rat sciatic nerve using cuff electrodes and macro-sieve electrodes
Full text linkTripolar cuff electrodes were designed, fabricated and non-chronically implanted in the sciatic nerve of two-month-old Lewis rats. A proximal constant current stimulus to the nerve was blocked by applying a high frequency sinusoidal signal to the distally placed tripolar cuff electrode. The frequency voltage characteristic of the blocking signal was obtained. Complete block was not achieved using variants of the tripolar cuff design and bipolar cuff electrodes. Single and dual macro-sieve electrode assemblies were designed, fabricated and chronically implanted in the sciatic nerve of Lewis rats. After a four-month period for regeneration four different electrode configurations were tested to enable a high frequency block. A complete and quickly reversible block was obtained using both the macro-sieve electrodes for the HFAC block – proximal macro-sieve as anode and distal macro-sieve as cathode. Finite element modelling and axon modelling was done to determine the optimal parameters for effecting a high frequency block in the nerve
ARTIFICIAL INTELLIGENCE FOR CLINICAL DECISION SUPPORT, DIAGNOSIS OF NEUROLOGICAL DISORDERS, INFECTIOUS DISEASE SURVEILLANCE, AND EARLY INTERVENTION
Full text linkThis dissertation explores innovative artificial intelligence approaches to detect and diagnose neurological disorders earlier and more effectively than current methods. The research addresses critical diagnostic delays in stroke, autism, COVID-19, brain cancer, and hypoxic-ischemic injury—conditions where early intervention significantly improves outcomes.
For stroke detection, a deep learning pipeline analyzes facial expressions in smartphone videos, achieving 76.1% accuracy using transformer networks with adversarial training. A complementary approach leverages large language models to identify interpretable linguistic markers in neurological disorders by analyzing transcripts of picture descriptions, revealing distinct patterns in patients with dementia and stroke.
For brain cancer detection during surgery, vision transformers analyze optical coherence tomography images with 98.6% accuracy, while minimizing patient-specific bias through adversarial training to improve generalizability. For autism detection, a computer vision pipeline analyzes naturalistic videos of 14-month-old infants, revealing significant differences in emotional state transitions between children later diagnosed with autism and neurotypical children, identifying markers 22 months before standard diagnosis.
In pandemic surveillance, a mobile application collecting self-reported symptoms enables early detection of COVID-19 outbreaks through space-time cluster analysis, providing a 5-day advance warning before confirmed cases surge. Finally, ultrasound neuromodulation combined with voltage-sensitive dye imaging demonstrates differential responses between normoxic and hypoxic neurons, establishing a foundation for early detection of hypoxic-ischemic neural injury.
The integration of advanced computational methods—deep learning, transformers, adversarial training, and spatio-temporal analysis—with novel sensing modalities creates powerful tools for earlier disease detection. These approaches demonstrate substantial improvements over conventional methods in accuracy, timeliness, and clinical utility, with the potential to transform neurological care through earlier intervention and improved patient outcomes
Under-reporting of income by wealthy Indians
No full textPRIFPRI5Nutrition, Diets, and Health (NDH); Food and Nutrition Polic
The impact of interventions delivered through farmer producer organizations on agricultural and empowerment outcomes in India
No full textIn India, farmer producer organizations (FPOs) promote collective action among small and marginalized farmers, especially women. By facilitating resource pooling and coordination of production activities, improving market access, and providing training and extension, FPOs can contribute to higher agricultural incomes for smallholder farmers. Women’s FPOs also help formalize women’s roles in agriculture, offer them remunerative employment, and enhance their engagement across the value chain. We use two rounds of panel data on 1,200 households in the eastern Indian state of Jharkhand to assess the impact of women-only FPOs. The FPO interventions we evaluate are threefold: strengthening collectives, coordinating and improving agricultural practices, and providing gender-based training. Women FPO members in the treatment arm received these interventions; those in the control arm did not. We match treatment and control blocks based on economic and developmental characteristics from secondary data sources, and then match individuals using baseline survey characteristics. We estimate impacts using difference-in-difference models with nearest neighbour matching techniques—with household assets, agricultural yields and profits, and women’s empowerment as our primary and secondary outcomes. We find some impact of FPO interventions on agricultural outcomes, but mixed evidence of their impact on women’s empowerment. This research contributes to the existing body of literature by rigorously assessing the impact of FPOs on agriculture-related and women’s empowerment–related outcomes. The findings will provide policymakers and practitioners with insights into designing strategies to promote sustainable agricultural growth and gender equit
Do wealthy politicians underreport their income? Evidence from general election data
No full textThe income reporting behaviour of wealthy Indians is a critical public finance issue. It has remained under-researched due to the lack of data sources required for the purpose. In this article, we use a new and unique source of information to examine the income reporting behaviour of politicians from across a wide range of wealth spectrums. The new dataset compiled and used by us is based on the affidavits filed by contestants in the 2014 and 2019 Lok Sabha elections. We find that, on average, wealthier candidates and their households report less income relative to their wealth. Consequently, most affluent families do not necessarily figure among those reporting the highest income to tax authorities. The income declared to tax authorities by the 10% least wealthy candidates is more than 300% of their wealth. In contrast, the income level reported by the wealthy group is a tiny fraction of their wealth. The wealthiest 5% of candidates have reported income amounting to only 3.4% of their wealth. The reported income of the wealthiest 0.1% is less than 2% of their wealth. The results are very similar for the households. We show that the abysmally low income reported by the wealthy groups stands in sharp contrast to the returns on assets owned by them. We argue that the missing income of the wealth groups is a result of the creative accounting and financial engineering used by them to avoid paying taxes
Collective analog bioelectronic computation
Full text linkThesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 677-710).In this thesis, I present two examples of fast-and-highly-parallel analog computation inspired by architectures in biology. The first example, an RF cochlea, maps the partial differential equations that describe fluid-membrane-hair-cell wave propagation in the biological cochlea to an equivalent inductor-capacitor-transistor integrated circuit. It allows ultra-broadband spectrum analysis of RF signals to be performed in a rapid low-power fashion, thus enabling applications for universal or software radio. The second example exploits detailed similarities between the equations that describe chemical-reaction dynamics and the equations that describe subthreshold current flow in transistors to create fast-and-highly-parallel integrated-circuit models of protein-protein and gene-protein networks inside a cell. Due to a natural mapping between the Poisson statistics of molecular flows in a chemical reaction and Poisson statistics of electronic current flow in a transistor, stochastic effects are automatically incorporated into the circuit architecture, allowing highly computationally intensive stochastic simulations of large-scale biochemical reaction networks to be performed rapidly. I show that the exponentially tapered transmission-line architecture of the mammalian cochlea performs constant-fractional-bandwidth spectrum analysis with O(N) expenditure of both analysis time and hardware, where N is the number of analyzed frequency bins. This is the best known performance of any spectrum-analysis architecture, including the constant-resolution Fast Fourier Transform (FFT), which scales as O(N logN), or a constant-fractional-bandwidth filterbank, which scales as O (N2).(cont.) The RF cochlea uses this bio-inspired architecture to perform real-time, on-chip spectrum analysis at radio frequencies. I demonstrate two cochlea chips, implemented in standard 0.13m CMOS technology, that decompose the RF spectrum from 600MHz to 8GHz into 50 log-spaced channels, consume < 300mW of power, and possess 70dB of dynamic range. The real-time spectrum analysis capabilities of my chips make them uniquely suitable for ultra-broadband universal or software radio receivers of the future. I show that the protein-protein and gene-protein chips that I have built are particularly suitable for simulation, parameter discovery and sensitivity analysis of interaction networks in cell biology, such as signaling, metabolic, and gene regulation pathways. Importantly, the chips carry out massively parallel computations, resulting in simulation times that are independent of model complexity, i.e., O(1). They also automatically model stochastic effects, which are of importance in many biological systems, but are numerically stiff and simulate slowly on digital computers. Currently, non-fundamental data-acquisition limitations show that my proof-of-concept chips simulate small-scale biochemical reaction networks at least 100 times faster than modern desktop machines. It should be possible to get 103 to 106 simulation speedups of genome-scale and organ-scale intracellular and extracellular biochemical reaction networks with improved versions of my chips. Such chips could be important both as analysis tools in systems biology and design tools in synthetic biology.by Soumyajit Mandal.Ph.D
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