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Zonal Distribution and Determinants of Stunting, Wasting, Underweight, and Thinness among the Indian Under-Five Children: Findings from NFHS-5
No full textBackground: The United Nations adopted the Sustainable Development Goals (SDGs) in 2015 with the aim of reducing the mortality rate for children under-five by at least 25 per 1000 live births worldwide. Four years later in 2019, about 824, 000 children died in India before their fifth birthday, the second-highest number worldwide. Most of them were due to undernutrition. Objective: The objective of the study was to estimate the prevalence of undernutrition among under-five children throughout India\u27s various zones as well as to find its contributing factors. Methods: The National Family Health Survey, India 2019–21 (NFHS-5) provided data on undernutrition and related variables of 221, 364 under-five children. Undernutrition in children was measured by stunting, wasting, underweight, and thinness. Socio-demographic and maternal characteristics were taken into consideration as influential factors. In order to determine the effect of these factors on childhood undernutrition, binary logistic regression was applied.Results: The analysis clearly showed that stunting was more prevalent in the East (38.51%) zone, whereas wasting (25.40%) underweight (37.26%) and thinness (23.07%) were more prevalent in the West zone. Children in each zone were found to have lower mean z-scores compared to WHO standards, indicating poor nutritional status. In all the zones, stunting and underweight were more common in anemic children. Undernutrition was more likely to occur in children whose mothers had lower educational attainment. The risk of undernutrition was higher in children from the scheduled caste (SC) and scheduled tribe (ST) communities. The children from poor families were more prone to be undernourished than children from wealthier families
Resource theory of imaginarity in distributed scenarios
No full textThe resource theory of imaginarity studies the operational value of imaginary parts in quantum states, operations, and measurements. Here we introduce and study the distillation and conversion of imaginarity in distributed scenario. This arises naturally in bipartite systems where both parties work together to generate the maximum possible imaginarity on one of the subsystems. We give exact solutions to this problem for general qubit states and pure states of arbitrary dimension. We present a scenario that demonstrates the operational advantage of imaginarity: the discrimination of quantum channels without the aid of an ancillary system. We then link this scenario to local operations and classical communications(LOCC) discrimination of bipartite states. We experimentally demonstrate the relevant assisted distillation protocol, and show the usefulness of imaginarity in the aforementioned two tasks
Role of future SNIa data from Rubin LSST in reinvestigating cosmological models
No full textWe study how future Type Ia supernovae (SNIa) standard candles detected by the Vera C. Rubin Observatory (LSST) can constrain some cosmological models. We use a realistic 3-yr SNIa simulated data set generated by the LSST Dark Energy Science Collaboration time domain pipeline, which includes a mix of spectroscopic and photometrically identified candidates. We combine these data with cosmic microwave background (CMB) and baryon acoustic oscillation (BAO) measurements to estimate the dark energy model parameters for two models – the baseline Lambda cold dark matter (CDM) and Chevallier–Polarski–Linder (CPL) dark energy parametrization. We compare them with the current constraints obtained from the joint analysis of the latest real data from the Pantheon SNIa compilation, CMB from Planck 2018 and BAO. Our analysis finds tighter constraints on the model parameters along with a significant reduction of correlation between H0 and σ8,0. We find that LSST is expected to significantly improve upon the existing SNIa data in the critical analysis of cosmological models
SafeSpeech: a three-module pipeline for hate intensity mitigation of social media texts in Indic languages
No full textWarning: This paper contains some abusive text that might sometimes be found offensive. Identifying and mitigating hateful, abusive, offensive comments on social media is a crucial, paramount task. It’s challenging to entirely prevent such hateful content and impose rigorous censorship on social platforms while safeguarding free speech. Recent studies have focused on detecting hate speech, whereas mitigating the intensity of hate remains unexplored or somewhat complex. This paper introduces a cost-effective, straightforward, and novel three-module pipeline, SafeSpeech, for Hate Speech Classification, Hate Intensity Identification, and Hate Intensity Mitigation on social media texts. The initial module classifies text as either containing or not containing hate speech. Following this, the second module quantifies the intensity of hate associated with individual words within the classified hate speech. Lastly, the third module seeks to diminish the overall hatefulness conveyed in the text. A comprehensive experiment has been conducted using publicly available datasets in five Indic languages (Hindi, Marathi, Tamil, Telugu, and Bengali). The system undergoes thorough evaluation to assess its performance and analyze it in-depth using various automated metrics. We evaluated the performance of Hate Speech Classification using Precision, Recall, and F1 metrics. For Hate Intensity Identification, we use human evaluation. Recognizing the limitations of automated metrics in mitigating hate speech, we augment our experiments with human evaluation for Hate Intensity Mitigation, where three domain experts independently participated. BERTScore for final generated hate-mitigated texts and first classified hate texts across all languages consistently range between 0.96 and 0.99
Scaling Effects of the Weissenberg Number in Electrokinetic Oldroyd-B Fluid Flow Within a Microchannel
No full textThis study attempts to extend previous research on electrokinetic turbulence (EKT) in Oldroyd-B fluid by investigating the relationship between the Weissenberg number ((Formula presented.)) and the second-order velocity structure function ((Formula presented.)) under applied electric fields. Inspired by Sasmal\u27s demonstration in Sasmal (2022) of how heterogeneous zeta potentials induce turbulence above a critical (Formula presented.), we develop a mathematical framework linking (Formula presented.) to turbulent phenomena. Our analysis incorporates recent findings on AC (Zhao & Wang, 2017) and DC (Zhao & Wang 2019) EKT, which have defined scaling laws for velocity and scalar structure functions in the forced cascade region. Our finding shows that (Formula presented.) and (Formula presented.), for a length scale (Formula presented.), and (Formula presented.), where (Formula presented.) is a velocity fluctuations quantity and (Formula presented.) denotes the time relaxation parameter. This work establishes a positive correlation between (Formula presented.) and turbulent flow phenomena through a rigorous analysis of velocity structure functions, thereby offering a mathematical foundation for building the design and optimization of EKT-based microfluidic devices
Self-cleaning formulations of mixed metal oxide-silver micro-nano structures with spiky coronae as antimicrobial coatings for fabrics and surfaces
No full textAntimicrobial coatings are essential for controlling the spread of pathogens and restricting their interface with hosts. These may be applied to masks as additional security to wearers over pore filtration or applied to surfaces that are often hand-touched, as the nanocoating can deactivate viruses irreversibly. We synthesized an antimicrobial nanoformulation containing mixed metal oxides (MMOs) of TiO2, ZnO, SiO2, and CuO with silver nanoparticles (MMO-Ag) capped with a cationic surfactant via a hydrothermal route. The developed nanoformulation possesses a high specific surface area of 73.5 m2 g−1. The nanoformulation exhibits excellent antimicrobial properties against Gram-negative (E. coli) and Gram-positive (S. aureus) bacteria, and bacteriophage viruses, superior to that of the spherical morphology. The minimum inhibition concentration (MIC) of the nanoformulation is 107 μg mL−1 against S. aureus. The enhanced antimicrobial properties of the spiky nanoformulation are attributed to its sharp nanometric tips that can physically puncture the cell membrane of pathogens via a mechano-bactericidal effect. The net attraction between the spiky MMO particle and bacteria is 102 times when compared to smooth MMO particles, as estimated by the modified DLVO theory. The developed nanoformulation-coated fabrics exhibit self-cleaning properties upon exposure to UV light by facilitating complete degradation of the bacteria owing to the photocatalytic component present in the nanoformulation that is enhanced by the electric field intensity near the tips, augmented by silver nanoparticles
Self-testing of genuine multipartite non-local and non-maximally entangled states
No full textSelf-testing is pivotal in characterizing quantum systems with minimal internal assumptions, representing the most robust certification method. In the existing self-testing literature, self-testing states that are not maximally entangled, but exhibit genuine multipartite nonlocality, have remained an open problem, a crucial issue given its significance in understanding many-body systems. Addressing this gap, we introduce a Cabello-like paradox applicable to scenarios involving any number of parties, offering a means to detect genuine multipartite nonlocality. This paradox facilitates the identification and self-testing of states that push its limits, notably non-maximally multipartite entangled states. While recent results (Šupić et al., 2023 [32]) suggest network self-testing as a means to self-test all quantum states, here we operate within the standard self-testing framework to self-test genuine multipartite non-local and non-maximally entangled states
Some remarks on two-periodic modules over local rings
No full textIn this paper, some properties of finitely generated two-periodic modules over commutative Noetherian local rings have been studied. We show that under certain assumptions on a pair of modules (M,N) with M being two-periodic, the natural map M ⊗ RN →HomR(M, ∗N) is an isomorphism. As a consequence, we prove that Auslander\u27s depth formula holds for such a Tor-independent pair. Tor-independence plays a crucial role for the depth formula to hold. Under certain assumptions on the modules, we show that a pair of modules, over a one-dimensional local ring, is Tor-independent if and only if their tensor product is torsion-free. Celikbas et al. recently showed the Huneke-Wiegand conjecture holds for two-periodic modules over one-dimensional domains. We generalize their result to the case of two-periodic modules with rank over one-dimensional local rings
Spatial dynamics of swarmalators’ movements
No full textThe simultaneous interactions of synchronization and swarming phenomena are the primary basis for the functioning of many natural systems, such as vinegar eels, sperms, and magnetic particles. Investigating the dual behavior of these particles, which are called swarmalators, has recently attracted the attention of many researchers. However, more needs to be done to analyze the dynamics of swarmalators separately. This paper explores the dynamics of the swarmalators\u27 movements individually over time in two-dimensional plane. This study\u27s findings represent that based on the two rules of high sensitivity to the initial conditions and the positive largest Lyapunov exponent, the swarmalators’ movements in the dynamic states have a chaotic nature. This result can lead to a deeper understanding of the complex dynamics of natural swarmalators. It may provide an opportunity for other researchers to analyze the individual behavior of swarmalators along with their collective behavior
TANet: Text region attention learning for vehicle re-identification
No full textIn recent years, the challenge of distinguishing vehicles of the same model has prompted a shift towards leveraging both global appearances and local features, such as lighting and rearview mirrors, for vehicle re-identification (ReID). Despite advancements, accurately identifying vehicles remains complex, particularly due to the underutilization of highly discriminative text regions. This paper introduces the Text Region Attention Network (TANet), a novel approach that integrates global and local information with a specific focus on text regions for improved feature learning. TANet uniquely captures stable and distinctive features across various vehicle views, demonstrating its effectiveness through rigorous evaluation on the VeRi-776, VehicleID, and VERI-Wild datasets. TANet significantly outperforms existing methods, achieving mAP scores of 83.6% on VeRi-776, 84.4% on VehicleID (Large), and 76.6% on VERI-Wild (Large). Statistical tests further validate the superiority of TANet over the baseline, showcasing notable improvements in mAP and Top-1 through Top-15 accuracy metrics