High risk scoring in pregnancy using modified Coopland’s scoring system and its association with perinatal outcome

Background: High-risk pregnancy is one in which the mother, foetus or the newborn has an elevated risk of experiencing an adverse outcome. These high-risk women form a special vulnerable cohort that can be identified in the antenatal period using a simple, easy to use, cost-effective tool- a maternal risk scoring system. Early identification of these high-risk mothers will facilitate effective intervention strategies to deal with the complications.Methods: This study was carried out on 300 pregnant women with gestational age more than 28 weeks. Detailed history, examination and necessary investigations were done and then using the Modified Coopland scoring system, each pregnant woman was assigned a risk score and stratified into 3 risk groups- low risk (0-3), moderate risk (4-6) and high risk (≥7) and followed up till delivery and 7 days postpartum. Subsequently, the maternal and perinatal outcomes were compared with their respective scores.Results: In this study, 14.66% patients belonged to the high-risk category. Statistically, a significant difference was noted in the number of low-birth-weight babies, in 5 minutes APGAR score <7 and in NICU admissions in the high-risk group compared to the low-risk group. Overall perinatal mortality was 13.33/1000 live births. In the high-risk group, a significant difference was seen in the occurrence of PPH and the need for operative delivery.Conclusions: Significant association between high-risk pregnancy and the poor maternal and perinatal outcome was noted. Therefore, a simple, cost-effective high-risk pregnancy scoring system such as the one proposed in this study can be used to identify potential high-risk pregnancies, provide them with tertiary care facilities and also corrective measures can be undertaken to prevent or minimize the complicating factors

Dynamic Voltage Scaling With Reduced Frequency Switching And Preemptions

Dynamic Voltage Scaling is an innovative technique for reducing the power consumption of a processor by utilizing its hardware functionality. Dynamic Voltage Scaling processors are mainly focusing on power management. Such processors can be switch between discrete frequency and voltage levels. The main challenges of Dynamic Voltage Scaling are increased number of preemptions and frequency switching. A part of dynamic energy as well as CPU time is lost due to these processes. To limit such processes, an algorithm is proposed which reduces both unwanted frequency switching and preemptions

Description of the male of Caligus hilase Shaw (Copepoda, Caligidae)

Caligus hil.rae Shen, 1957, is unique in the possession of a comparatively very long four-segmented abdomen, long anal laminae which are nearly three times as long as broad and in the absence of the sternal fork

Marine fishing practices and coastal aquaculture technologies in India

Among the countries bordering the Indian Ocean, India, endowed with 2.02 million sq. km of EEZ along a coastline of8129 km and 0.5 million sq. km of continental shelf with a catchable annual marine fishery potential of 3.93 million tonncs occupies a unique position. Besides, there are vast brackishwater spread areas along the coastline which offer ideal sites for seafamling and coastal mariculture

Note on the mottled fusilier Dipterygonotus balteatus (Valenciennes, 1830) landed along Tamil Nadu coast

Unusual landing of the mottled fusilier Dipterygonotus balteatus was observed along the northern coast of Tamil Nadu during July and September 2006. Mottled fusiliers belong to the percoid family Caesionidae. While the juveniles are known to school together with other caesionids on coral reefs, the adults are usually distributed in near shore pelagic waters. D. balteatus occurs in Indo- Pacific waters, from East Africa, off Lakshadweep, Sri Lanka, Indonesia, to Philippines, Indo-China and northern Australia

Ecology and biology of Abudefduf glaucus (Cuvier) (Pomacentridae, Pisces) from Minicoy atoll, Lakshadweep

Abudefduf glaucus forms a very conspicuous component of the upper reef flat idhyofaunal assemblage throughout Lakshadweep. It is found living under crevices of reef rocks and under the dead boulders and often swimming out in small shoals. The fish is sluggish. Juveniles settle at first in small upper littoral rock [x>ols. The dispersal takes place when it attains a total length of 30 to 40 mm. The spcdcs is essentially a herbivore, feeding on small encrusting and filamentous algae found attached to reef rocks, though sometimes copcpods and calcareous material are found in the gut. The fish is active during the day time. A common formula for both the males and females to establish the length -weight relationship is given as follows : Log W = -3.773507 + 2.534044 Log L

Ecological stress in Minicoy Lagoon and it's impact on tuna live-baits

In the present communication the authors make an attempt to throw more light on the ecology and biology of reef fishes from Lakshadweep, especially on the impact of ecological stress in the Minicoy Lagoon on the tuna live-baits

Multilevel Importance Sampling for Rare Events Associated With the McKean--Vlasov Equation

This work combines multilevel Monte Carlo (MLMC) with importance sampling to estimate rare-event quantities that can be expressed as the expectation of a Lipschitz observable of the solution to a broad class of McKean--Vlasov stochastic differential equations. We extend the double loop Monte Carlo (DLMC) estimator introduced in this context in (Ben Rached et al., 2023) to the multilevel setting. We formulate a novel multilevel DLMC estimator and perform a comprehensive cost-error analysis yielding new and improved complexity results. Crucially, we devise an antithetic sampler to estimate level differences guaranteeing reduced computational complexity for the multilevel DLMC estimator compared with the single-level DLMC estimator. To address rare events, we apply the importance sampling scheme, obtained via stochastic optimal control in (Ben Rached et al., 2023), over all levels of the multilevel DLMC estimator. Combining importance sampling and multilevel DLMC reduces computational complexity by one order and drastically reduces the associated constant compared to the single-level DLMC estimator without importance sampling. We illustrate the effectiveness of the proposed multilevel DLMC estimator on the Kuramoto model from statistical physics with Lipschitz observables, confirming the reduced complexity from $\mathcal{O}(\mathrm{TOL}_{\mathrm{r}}^{-4})$ for the single-level DLMC estimator to $\mathcal{O}(\mathrm{TOL}_{\mathrm{r}}^{-3})$ while providing a feasible estimate of rare-event quantities up to prescribed relative error tolerance $\mathrm{TOL}_{\mathrm{r}}$.Comment: Follow-up to arXiv:2207.0692

Double-Loop Importance Sampling for McKean--Vlasov Stochastic Differential Equation

This paper investigates Monte Carlo (MC) methods to estimate probabilities of rare events associated with solutions to the $d$-dimensional McKean-Vlasov stochastic differential equation (MV-SDE). MV-SDEs are usually approximated using a stochastic interacting $P$-particle system, which is a set of $P$ coupled $d$-dimensional stochastic differential equations (SDEs). Importance sampling (IS) is a common technique for reducing high relative variance of MC estimators of rare-event probabilities. We first derive a zero-variance IS change of measure for the quantity of interest by using stochastic optimal control theory. However, when this change of measure is applied to stochastic particle systems, it yields a $P \times d$-dimensional partial differential control equation (PDE), which is computationally expensive to solve. To address this issue, we use the decoupling approach introduced in [dos Reis et al., 2023], generating a $d$-dimensional control PDE for a zero-variance estimator of the decoupled SDE. Based on this approach, we develop a computationally efficient double loop MC (DLMC) estimator. We conduct a comprehensive numerical error and work analysis of the DLMC estimator. As a result, we show optimal complexity of $\mathcal{O}(\mathrm{TOL}_{\mathrm{r}}^{-4})$ with a significantly reduced constant to achieve a prescribed relative error tolerance $\mathrm{TOL}_{\mathrm{r}}$. Subsequently, we propose an adaptive DLMC method combined with IS to numerically estimate rare-event probabilities, substantially reducing relative variance and computational runtimes required to achieve a given $\mathrm{TOL}_{\mathrm{r}}$ compared with standard MC estimators in the absence of IS. Numerical experiments are performed on the Kuramoto model from statistical physics

Multi-index Importance Sampling for McKean-Vlasov Stochastic Differential Equation

This work introduces a novel approach that combines the multi-index Monte Carlo (MC) method with importance sampling (IS) to estimate rare event quantities expressed as an expectation of a smooth observable of solutions to a broad class of McKean-Vlasov stochastic differential equations. We extend the double loop Monte Carlo (DLMC) estimator, previously introduced in our works (Ben Rached et al., 2022a,b), to the multi-index setting. We formulate a new multi-index DLMC estimator and conduct a comprehensive cost-error analysis, leading to improved complexity results. To address rare events, an importance sampling scheme is applied using stochastic optimal control of the single level DLMC estimator. This combination of IS and multi-index DLMC not only reduces computational complexity by two orders but also significantly decreases the associated constant compared to vanilla MC. The effectiveness of the proposed multi-index DLMC estimator is demonstrated using the Kuramoto model from statistical physics. The results confirm a reduced complexity from $\mathcal{O}(\mathrm{TOL}_{\mathrm{r}}^{-4})$ for the single level DLMC estimator (Ben Rached et al., 2022a) to $\mathcal{O}(\mathrm{TOL}_{\mathrm{r}}^{-2} (\log \mathrm{TOL}_{\mathrm{r}}^{-1})^2)$ for the considered example, while ensuring accurate estimation of rare event quantities within the prescribed relative error tolerance $\mathrm{TOL}_\mathrm{r}$.Comment: Extension to works 2207.06926 and 2208.0322