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Introduction:Fetal weight, in conjunction with gestational age, is an important indicator of pregnancy outcome.Accurate estimation of fetal weight is of paramount importance in the management of labor and delivery and it also help in avoidance of complications associated with fetal excessive or low-birth-weight babies, thereby decreasing perinatal morbidity and mortality. Various clinical formulae like Johnson’s formula & Dare’s formula are in use for fetal weight estimation. Aim of the study was to assess the fetal weight in term pregnancy by various clinical methods- Dare’s formula & Johnson’s formula and its comparison and correlation with actual birth weight.Materials & Method: It was a prospective observational study of 334 women at term pregnancy at GMERS medical college & Hospital, Vadnagar from April 2021 to July 2021. The formulas used in this study were: Johnson’s formula and Dare’s formula. The measurements were compared with actual birth weight after the birth of baby.Results: The mean actual birth weight was 2759.07±466.15 grams. The mean estimated birth weight by Dare’s and Johnson’s method was 2845.79±453.43 grams and grams 2990.81±413.68respectively. Dare’s method was more accurate and had least maximum and minimum error than Johnson’s method.Conclusion: Dare’s formula is an inexpensive method for fetal birth weight estimation. It can be used on large scale because of its low cost, ease of use, and need for little training as the setup for ultra sonographic evaluation is not readily available in rural setups. Keywords: Abdominal Girth, Actual birth weight, Dare’s formula, Estimated birth weight, Johnson’s formula, Symphysio-fundal height.

Prediction of next message after given a sequence of messages has lot of applications. It has applications in NLP (natural language processing), Log Anomaly Detection, Automating Customer Service and many more. Automating Customer Service is one such application which shall be in great focus in this paper. For any organization, automating customer service can save a lot of revenue and time. It increases the response time for customers/users, which in turn increases customer satisfaction. It reduces the load on the business. In this paper, we use machine learning algorithm (Support vector machine + gridsearchcv) to automate the next debug message or step that has to be followed to solve a customer problem/ticket. This algorithm can be used to predict multiple next steps also, thereby automating the debug steps handled by the customer service team. It predicts the next debug message given a sequence at an accuracy of 98.08 percent.

This Interventions essay presents 14 stories of, and positions on, urban climates in South Asia. We look analytically and linguistically from this region to engage the terms ‘mahaul’, ‘mausam’ and ‘aab-o-hawa’ as critical concepts to conceptualize climate in its political, social, historic, atmospheric, ecological, material, sensory and embodied registers. Gathered together, the stories scaffold a perspective on climate that connects concerns about broader structural conditions (mahaul); local and lived experiences in different temporal registers (mausam) and sociomaterial entanglements that demand new ways of knowing nature (aab-o-hawa). An expansive yet grounded conceptualization allows us to narrate individual cases and local climate stories in their multiplicity and difference, rather than through cumulative effects across much wider geographies. This essay on South Asian urban climates provides an analytical frame based on shared colonial history, and geographies connecting experiences of climate across fraught geopolitical borders. These diverse South Asian urbanisms provide evidence of a range of environmental vulnerabilities, while seeking possibilities in already existing climates—in the seas and airs that reorient the experience of land and atmosphere, in centering marginalized voices, in historical remnants to read contemporary urban change, in exploring planning agency grounded in local politics, and from the position of partial knowledge that being within urban climates entails.

Contains fulltext : 288038.pdf (Publisher’s version ) (Open Access) BACKGROUND: COVID-19 vaccine-induced antibody responses are reduced in patients with inflammatory bowel disease (IBD) taking anti-TNF or tofacitinib after two vaccine doses. We sought to assess whether immunosuppressive treatments were associated with reduced antibody and T-cell responses in patients with IBD after a third vaccine dose. METHODS: VIP was a multicentre, prospective, case-control study done in nine centres in the UK. We recruited immunosuppressed patients with IBD and non-immunosuppressed healthy individuals. All participants were aged 18 years or older. The healthy control group had no diagnosis of IBD and no current treatment with systemic immunosuppressive therapy for any other indication. The immunosuppressed patients with IBD had an established diagnosis of Crohn's disease, ulcerative colitis, or unclassified IBD using standard definitions of IBD, and were receiving established treatment with one of six immunosuppressive regimens for at least 12 weeks at the time of first dose of SARS-CoV-2 vaccination. All participants had to have received three doses of an approved COVID-19 vaccine. SARS-CoV-2 spike antibody binding and T-cell responses were measured in all participant groups. The primary outcome was anti-SARS-CoV-2 spike (S1 receptor binding domain [RBD]) antibody concentration 28-49 days after the third vaccine dose, adjusted by age, homologous versus heterologous vaccine schedule, and previous SARS-CoV-2 infection. The primary outcome was assessed in all participants with available data. FINDINGS: Between Oct 18, 2021, and March 29, 2022, 352 participants were included in the study (thiopurine n=65, infliximab n=46, thiopurine plus infliximab combination therapy n=49, ustekinumab n=44, vedolizumab n=50, tofacitinib n=26, and healthy controls n=72). Geometric mean anti-SARS-CoV-2 S1 RBD antibody concentrations increased in all groups following a third vaccine dose, but were significantly lower in patients treated with infliximab (2736·8 U/mL [geometric SD 4·3]; p

Contains fulltext : 291656.pdf (Publisher’s version ) (Open Access)

Zn is a promising biodegradable metal that shows huge potential as bioresorbable implant material as it possesses outstanding biocompatibility and high corrosion resistance than Mg. However, the low value of mechanical strength and hardness has hugely restricted its application. Moreover, incorporating alloying elements have typically magnified its mechanical properties. In the current study, the effect of the alloying component Mn and HA on the Zn-Mg composite and also the effect of polymer-ceramics nanofiber coating on the composite sample was studied. The result shows that the current studied samples were mainly comprised of a primary Zn matrix and a secondary phase of Mg

In this chapter we look into a high-order representation of complex geometries by the Brinkman penalization method. We focus on the effect of this immersed boundary model on the boundary layers in a high-order discontinuous Galerkin scheme. High-order approximations are attractive on modern computing architectures, as they require few degrees of freedom, to represent smooth solutions, resulting in a smaller memory footprint when compared to lower order discretizations. A significant hurdle in using high-order methods for problems involving complex geometries is matching the surface description of the geometry with the discretization scheme. The Brinkman penalization offers a method to achieve this without the need for complicated mesh generation and special elements. We investigated its use in our high-order discontinuous Galerkin implementation Ateles in [2], where we looked at inviscid effects like reflections. Here we investigate the viscous boundary layer close to a wall, modeled by the penalization.

In this paper, a sequence-to-sequence model is proposed for translating sentences without idiom to sentence with the idiom. The problem is challenging in two ways, predicting the correct idiom based on context and generating the correct sentence using the idiom due to complex semantic and syntactic rules of language. Sequence-to-sequence learning has gained popularity in the past few years due to their surprising results on machine translation task. This work is based on sequence-to-sequence learning of word sequences along with their part-of-speech tags to predict sentences with correct idiomatic phrases. Results have shown that models have achieved higher BLEU score on using part-of-speech tags as the input sequences. These observations show the prominence of part-of-speech tags in identifying the hidden writing patterns in the language.

Trajectory and performance of launch vehicles are significantly affected by various flight parameters related to propulsion, aerodynamic forces, mass, center of gravity, etc. Precise estimation of these parameters during post-flight analysis is essential for continuous performance improvement of launch vehicles. Since a large number of parameters affect the performance of a launch vehicle, the estimation process becomes computationally complex and time consuming. Estimation of these parameters can be automated by using 6D simulation software which works in tandem with a heuristic method. In this study, an integrated computational procedure has been designed, which employs a 6D simulation software and a genetic algorithm-based optimizer, to estimate the input flight parameters from the actual flight data. The parameters to be estimated, population size, parameter bounds and genetic algorithm parameters such as crossover probability and mutation probability are taken as inputs to the estimation program along with flight trajectory profiles. The objective function to be minimized is derived by computing the squared error between the actual flight trajectory profiles and the simulated trajectory profiles. Since flight measured data contains noise, data smoothing techniques are implemented before usage to avoid trap down in local minima during optimization. Further, to reduce computational time, a relation matrix between input parameters and output flight profiles is devised to intelligently prioritize the parameter estimation. This technique is demonstrated against known input parameter variations and the estimated outputs are found to be very effective.

Recent pandemic situation of covid-19 which is caused due to the outbreak of highly dangerous and easily spreadable corona virus is an increasing concern throughout the entire world. The purpose of this study is to develop an alert system which alerts employees to maintain social distance in corporate working environments. A real-time location systems (RTLS) based on ultra wide band (UWB) wireless technology gives most accurate locations of approximately 10cm using methods like trilateration and TDOA (Time Difference of Arrival). Co-ordinates of the location can be obtained by installing RTLS in predefined area which are used to calculate the distance between Mobile UWB Devices (MUD’s). An alert triggered by a system to maintain distance if distance between the employees is less than the prescribed social distance can keep the work premises safe and control the spread of corona virus. This study can be a great solution to control spread of virus in corporate working environments which are mostly of confined in size and indoor in nature.

This paper aims at enhancing the thermo-fluid performance of a plain fin-and-tube heat exchanger. Influence of the height of rectangular type vortex generator on the heat exchanger performance has been analysed. In dimensionless form, the heat transfer is expressed as Colburn factor (j) and pressure drop in terms of the friction factor (f). The arrangement of all the vortex generators follows a “common-flow-up” configuration which tends to counter-rotate the vortices in the wake region of the tubes, thereby, enhancing the thermal mixing to improve the heat transfer. Another parameter, called quality factor (a ratio of j and f), is also evaluated to compare the results between different heights of the winglets. It has been observed that the j factor is the highest for the height 2.8 mm and f factor is the lowest for the height 0.7 mm, whereas the quality factor suggests the height of 0.7 and 1.4 mm to be the appropriate ones.

In this work we investigate the Brinkman volume penalization technique in the context of a high-order Discontinous Galerkin method to model moving wall boundaries for compressible fluid flow simulations. High-order approximations are especially of interest as they require few degrees of freedom to represent smooth solutions accurately. This reduced memory consumption is attractive on modern computing systems where the memory bandwidth is a limiting factor. Due to their low dissipation and dispersion they are also of particular interest for aeroacoustic problems. However, a major problem for the high-order discretization is the appropriate representation of wall geometries. In this work we look at the Brinkman penalization technique, which addresses this problem and allows the representation of geometries without modifying the computational mesh. The geometry is modelled as an artificial porous medium and embedded in the equations. As the mesh is independent of the geometry with this method, it is not only well suited for highorder discretizations but also for problems where the obstacles are moving.We look into the deployment of this strategy by briefly discussing the Brinkman penalization technique and its application in our solver and investigate its behavior in fundamental one-dimensional setups, such as shock reflection at a moving wall and the formation of a shock in front of a piston. This is followed by the application to setups with two and three dimensions, illustrating the method in the presence of curved surfaces.

We use Lightcone Conformal Truncation (LCT) -- a version of Hamiltonian truncation -- to study the nonperturbative, real-time dynamics of $\phi^4$-theory in 2+1 dimensions. This theory has UV divergences that need to be regulated. We review how, in a Hamiltonian framework with a total energy cutoff, renormalization is necessarily \emph{state-dependent}, and UV sensitivity cannot be canceled with standard local operator counterterms. To overcome this problem, we present a prescription for constructing the appropriate state-dependent counterterms for (2+1)d $\phi^4$-theory in lightcone quantization. We then use LCT with this counterterm prescription to study $\phi^4$-theory, focusing on the $\mathbb{Z}_2$ symmetry-preserving phase. Specifically, we compute the spectrum as a function of the coupling and demonstrate the closing of the mass gap at a (scheme-dependent) critical coupling. We also compute Lorentz-invariant two-point functions, both at generic strong coupling and near the critical point, where we demonstrate IR universality and the vanishing of the trace of the stress tensor., Comment: 33 pages + appendices, 14 figures; references added

India imposed a nationwide lockdown from 25th March 2020 onwards to combat the spread of COVID-19 pandemic. To model the spread of a disease and to predict its future course, epidemiologists make use of compartmental models such as the $$SIR$$ model. In order to address some of the assumptions of the standard $$SIR$$ model, a new modified version of $$SIR$$ model is proposed in this paper that takes into account the percentage of infected individuals who are tested and quarantined. This approach helps overcome the assumption of homogenous mixing of population which is inherent to the conventional $$SIR$$ model. Using the available data of the number of COVID-19 positive cases reported in the state of Kerala, and in India till 26th April, 2020 and 12th May 2020, respectively, the parameter estimation problem is converted into an optimization problem with the help of a least squared cost function. The optimization problem is then solved using differential evolution optimizer. The impact of lockdown is quantified by comparing the rising trend in infections before and during the lockdown. Using the estimated set of parameters, the model predicts that in the state of Kerala, by using certain interventions the pandemic can be successfully controlled latest by the first week of July, whereas the $${R}_{0}$$ value for India is still greater than 1, and hence lifting of lockdown from all regions of the country is not advisable.

In this paper, the architecture for idiom recommendation and its implementation based on the syntactical structure of phrases in sentences is proposed. Idioms are a bunch of words that have figurative meaning if taken together, which is different from the actual meaning of individual elements. The entire methodology is divided into four segments—segmentation, analysis, extraction and transformation. Emphasis on every possible syntactic structure of phrases is laid, that indicates similar implication to the structures in the dictionary and recommends idioms based on that.

Face Recognition is immensely proliferating as a research area in the paradigm of Computer Vision as it provides an extensive choice of applications in surveillance and commercial domains. This paper throws light upon the comparison of various dense feature descriptors (Dense SURF, Dense SIFT, Dense ORB) with each other and also with their classical counterparts (SURF, SIFT, ORB) using a novel technique for recognition. This proposed technique uses Laplacian of Gaussian filter for enhancement of the image. It applies various dense and classical feature descriptors on the enhanced image and outputs a feature vector. In order to achieve high performance, this feature vector is given to Fisher vector since Fisher Vector is a feature patch-aggregation method. Finally, extended nearest neighbor Classifier is used for classification over the orthodox k-nearest classifier. Experiments were carried out on three diverse datasets—ORL, Faces94, and Grimace. On scrutinizing the results, Dense SIFT and Dense ORB were found to be preeminent as measured by various performance metrics. 98.44 on Grimace, 98.15 on Faces94.

In the current techno-savvy world, cybersecurity is a prime concern. Biometrics are being extensively used for authentication and authorization. Face recognition(FR) is a class of biometrics which has proved to be one of the most effective methods for identification and verification, which works even when the subject is unaware of being scanned. This paper gives insight into the use of Image Processing in Robotic Applications. It discusses the use of computer vision by surveillance robots. Surveillance applications prefer unsupervised learning over supervised learning as unsupervised learning doesn’t require true labels. The burgeoning demand for unsupervised learning in surveillance applications proffered the nub of this project. The paper coalesces the well-known similarity algorithm SSIM with Spectral Clustering to produce prodigious results. SSIM surpasses other techniques like MSE by extracting structural features from images. This leads to a significant improvement in performance because humans also extract structural information from visuals. SSIM eliminates the effect of illumination and then uses the attributes that depict the structure of objects to gain the desired structural information. The performance of the proposed model was compared with other similarity measures on the ORL(Olivetti Research Laboratory Cambridge), Caltech and Faces96 datasets. An accuracy of 89.5% was achieved on the ORL and 88.3% on the Caltech and 86.7% on the Faces96 dataset.