A parabolized Navier-Stokes method for wind farm applications

Fluid flow simulations play an important role in the wind industry. With the development of large wind farms, flow simulations through an entire wind farm are becoming a necessity. These are required for designing the layout of new wind farms in the development stage and for forecasting power production from the existing ones for operational purposes. Conventional Navier-Stokes simulations (commonly referred to as CFD simulations) are computationally very expensive since they require a supercomputer with runtimes of several weeks. A Parabolized Navier-Stokes (PNS) method is developed and implemented in this study. The developed PNS method requires less stringent approximations as compared to the existing parabolic methods and incorporates more physics. A wind turbine model is developed and coupled with the PNS method for simulating wind turbines and entire wind farms. The wind turbine model is adapted for spatial marching and is based on the Actuator Line rotor model and Blade Element Momentum theory. The developed PNS method is validated and verified using several test cases and a wind farm simulation on a desktop computer has a runtime of only several hours

Mechanical Characterization of Adhesively Bonded Jute Composite Joints under Monotonic and Cyclic Loading Conditions

Fiber-reinforced composites comprise an important class of lightweight materials which are finding increasing applications in engineering structures including body components of automobiles and aircraft. Traditionally, synthetic fibers made of glass, carbon, etc. along with a polymeric resin have constituted the most common composites. However, due to environmental concern, occupational health safety considerations, higher cost, etc., research has been focused on substituting synthetic fibers, especially glass fibers with safer, economic and biodegradable natural fibers. Due to the ease of availability and affordability in terms of cost, woven jute mats, among a wide variety of natural fiber-based reinforcements, offer a good choice in combination with a suitable resin such as polyester or epoxy for fabrication of composite laminates. In structural applications, joining of parts made of jute fiber-reinforced composites (JFRCs) would be a natural requirement. Alternatives to joining processes for metals such as welding, riveting, etc. are required for composites. A joining process of high potential is adhesive bonding which has the advantages of reducing stress concentration, permitting fastening of dissimilar materials, etc. In the present study, adhesively bonded joints of JFRCs and their mechanical behavior are investigated under quasi-static and cyclic loading conditions. Initially, characterization of substrates is carried out under monotonic loading. This is followed by determination of stress- Strain curves, failure load and mean shear strength of bonded joints as functions of joint curing temperature and overlap length using a two-part structural epoxy adhesive. All tests are carried out according to relevant ASTM standards. It has been observed that higher curing temperatures give rise to only marginally high failure load and mean shear stress at failure compared to curing at room temperature. For a given curing temperature, failure load increases while mean shear strength decreases with respect to overlap length in both types of joints. As fatigue failure is a crucial consideration in design, the behavior of adhesively bonded JFRC joints is studied for the first time under cyclic loading conditions leading to the commonly-used S-N curve for characterization of failure of materials at different loading-unloading cycles. Interestingly, the fatigue strength for infinite life of adhesively bonded JFRC joints turns out to be approximately 30% of the quasi-static strength, a correlation which usually applies to materials in general. The effect of joint overlap length on fatigue life is studied and it is observed that the above relation between fatigue and quasi static strength is retained for different overlap lengths. Additionally, insights are provided into failure modes of joints under different loading conditions and for varying overlap lengths. Various empirical predictors such as exponent, power and hybrid models fitting the S-N curve are obtained and their relative efficacy (in terms of Coefficient of Determination R2, Adjusted-R2, Akaike’s Information Criterion and Residual Sum of Squares) enumerated in prediction of failure load including quasi-static failure load. As numerical simulation is an indispensable tool in designing geometrically complex structures under nonlinear conditions including failure and contact, finite element modeling of JFRC substrates, bulk adhesive and adhesively bonded joints has been investigated using implicit and explicit LS-DYNA solvers. In this context, the effects of various modeling parameters (mesh size and loading rate) and details of constitutive models capable of capturing plasticity and failure in an orthotropic composite and isotropic adhesive are discussed. Mesh size has been found to be an important parameter affecting computed results. Finally, a good correlation within ~(4% - 7%) was found between the predicted and experimental results for JFRC substrates, bulk adhesive and adhesively bonded single lap joints

Semantic SQL -- Combining and optimizing semantic predicates in SQL

In recent years, the surge in unstructured data analysis, facilitated by advancements in Machine Learning (ML), has prompted diverse approaches for handling images, text documents, and videos. Analysts, leveraging ML models, can extract meaningful information from unstructured data and store it in relational databases, allowing the execution of SQL queries for further analysis. Simultaneously, vector databases have emerged, embedding unstructured data for efficient top-k queries based on textual queries. This paper introduces a novel framework SSQL - Semantic SQL that utilizes these two approaches, enabling the incorporation of semantic queries within SQL statements. Our approach extends SQL queries with dedicated keywords for specifying semantic queries alongside predicates related to ML model results and metadata. Our experimental results show that using just semantic queries fails catastrophically to answer count and spatial queries in more than 60% of the cases. Our proposed method jointly optimizes the queries containing both semantic predicates and predicates on structured tables, such as those generated by ML models or other metadata. Further, to improve the query results, we incorporated human-in-the-loop feedback to determine the optimal similarity score threshold for returning results

Study of incidence and severity of menopausal symptoms in women of sub Himalayan region, using the Greene Climacteric Scale

Background: Identifying and measuring menopausal symptoms using Greene Climacteric Scale and calculating the mean age at menopause to find out the frequency of the menopausal symptoms so that can be used for better perimenapausal and menopausal care to females.Methods: A cross-sectional study was conducted in a tertiary care center in northern India. All menopausal women in gynaecology outpatient department were enrolled in study, over six months from May 2019 to October 2019. A total of 206 women fulfilled the inclusion criteria and were interviewed using the 21 points Greene Climateric Scale (GCS) Questionnaire. Analysis was done using SPSS 20 Windows software. Descriptive statistics included computation of percentages, means and standard deviations. Level of significance was set at P≤0.05.Results: The mean age of menopause was 47.9±3.42 years. About 90.3% of the menopausal women studied belonged to the rural population. The most frequently perceived symptoms by females were muscle joint pain (100%), vaginal dryness and pruritus vulvae (84%), lower abdominal pain (79.6%), hot flushes (50.5%).The most frequently reported symptoms as per the GCS were muscle and joint pains, loss of interest in sex, headaches, feeling tired or lacking in energy, difficulty in concentrating, attacks of anxiety, difficulty in sleeping and hot flushes. The mean total score was 17.61.Conclusions: Menopausal symptoms were common in this study group but women seeking help for the same was less. Therefore menopause clinics and care programmes need to be developed and strengthened to promote better health and higher quality of life in menopausal women

An Uncommon yet Treatable Cause of Hypoglycemia

Hypoglycemia is a life-threatening condition, especially if recurrent. Most common causes include patients with diabetes due to medications, nephropathy with oral hypoglycemic drugs, faulty dietary habits and other endocrine causes. In a patient presenting with recurrent hypoglycemia with central hypothyroidism, Sheehan syndrome must be suspected as diagnosis can prevent disastrous complication

Estimating Time to Clear Pendency of Cases in High Courts in India using Linear Regression

Indian Judiciary is suffering from burden of millions of cases that are lying pending in its courts at all the levels. The High Court National Judicial Data Grid (HC-NJDG) indexes all the cases pending in the high courts and publishes the data publicly. In this paper, we analyze the data that we have collected from the HC-NJDG portal on 229 randomly chosen days between August 31, 2017 to March 22, 2020, including these dates. Thus, the data analyzed in the paper spans a period of more than two and a half years. We show that: 1) the pending cases in most of the high courts is increasing linearly with time. 2) the case load on judges in various high courts is very unevenly distributed, making judges of some high courts hundred times more loaded than others. 3) for some high courts it may take even a hundred years to clear the pendency cases if proper measures are not taken. We also suggest some policy changes that may help clear the pendency within a fixed time of either five or fifteen years. Finally, we find that the rate of institution of cases in high courts can be easily handled by the current sanctioned strength. However, extra judges are needed only to clear earlier backlogs.Comment: 12 pages, 9 figures, JURISIN 2022. arXiv admin note: text overlap with arXiv:2307.1061

EHI: End-to-end Learning of Hierarchical Index for Efficient Dense Retrieval

Dense embedding-based retrieval is now the industry standard for semantic search and ranking problems, like obtaining relevant web documents for a given query. Such techniques use a two-stage process: (a) contrastive learning to train a dual encoder to embed both the query and documents and (b) approximate nearest neighbor search (ANNS) for finding similar documents for a given query. These two stages are disjoint; the learned embeddings might be ill-suited for the ANNS method and vice-versa, leading to suboptimal performance. In this work, we propose End-to-end Hierarchical Indexing -- EHI -- that jointly learns both the embeddings and the ANNS structure to optimize retrieval performance. EHI uses a standard dual encoder model for embedding queries and documents while learning an inverted file index (IVF) style tree structure for efficient ANNS. To ensure stable and efficient learning of discrete tree-based ANNS structure, EHI introduces the notion of dense path embedding that captures the position of a query/document in the tree. We demonstrate the effectiveness of EHI on several benchmarks, including de-facto industry standard MS MARCO (Dev set and TREC DL19) datasets. For example, with the same compute budget, EHI outperforms state-of-the-art (SOTA) in by 0.6% (MRR@10) on MS MARCO dev set and by 4.2% (nDCG@10) on TREC DL19 benchmarks

Qualitative Insights Tool (QualIT): LLM Enhanced Topic Modeling

Topic modeling is a widely used technique for uncovering thematic structures from large text corpora. However, most topic modeling approaches e.g. Latent Dirichlet Allocation (LDA) struggle to capture nuanced semantics and contextual understanding required to accurately model complex narratives. Recent advancements in this area include methods like BERTopic, which have demonstrated significantly improved topic coherence and thus established a new standard for benchmarking. In this paper, we present a novel approach, the Qualitative Insights Tool (QualIT) that integrates large language models (LLMs) with existing clustering-based topic modeling approaches. Our method leverages the deep contextual understanding and powerful language generation capabilities of LLMs to enrich the topic modeling process using clustering. We evaluate our approach on a large corpus of news articles and demonstrate substantial improvements in topic coherence and topic diversity compared to baseline topic modeling techniques. On the 20 ground-truth topics, our method shows 70% topic coherence (vs 65% & 57% benchmarks) and 95.5% topic diversity (vs 85% & 72% benchmarks). Our findings suggest that the integration of LLMs can unlock new opportunities for topic modeling of dynamic and complex text data, as is common in talent management research contexts.6 pages, 4 tables, 1 figur

Multi-modal Extreme Classification

This paper develops the MUFIN technique for extreme classification (XC) tasks with millions of labels where datapoints and labels are endowed with visual and textual descriptors. Applications of MUFIN to product-to-product recommendation and bid query prediction over several millions of products are presented. Contemporary multi-modal methods frequently rely on purely embedding-based methods. On the other hand, XC methods utilize classifier architectures to offer superior accuracies than embedding only methods but mostly focus on text-based categorization tasks. MUFIN bridges this gap by reformulating multi-modal categorization as an XC problem with several millions of labels. This presents the twin challenges of developing multi-modal architectures that can offer embeddings sufficiently expressive to allow accurate categorization over millions of labels; and training and inference routines that scale logarithmically in the number of labels. MUFIN develops an architecture based on cross-modal attention and trains it in a modular fashion using pre-training and positive and negative mining. A novel product-to-product recommendation dataset MM-AmazonTitles-300K containing over 300K products was curated from publicly available amazon.com listings with each product endowed with a title and multiple images. On the all datasets MUFIN offered at least 3% higher accuracy than leading text-based, image-based and multi-modal techniques. Code for MUFIN is available at https://github.com/Extreme-classification/MUFI