A PWM Method for Reducing dv/dt and Switching Losses in Two-Stage Power Converters

Today\u27s semiconductor devices are accompanied by high switching frequencies (\u3e kilo-hertz) and small transition times (\u3c micro-seconds). Such fast transition times are accompanied by undesirable effects such as voltage overshoots at the load terminals, ground leakage currents, wide-band electromagnetic noise, etc. With the advent of wide band-gap devices, several applications are moving towards higher switching frequency operation with at-least an order of magnitude reduction in transition times. While these characteristics are considered necessary to break the next-generation barriers in power density, efficiency and applicability, the undesirable effects due to faster transitions are expected to present obstacles. This work proposes a PWM approach to modify the shape of the switching voltages to overcome the disadvantages of the fast transition times without any increase in switching losses. In fact, several of the switching transitions feature ZVS operation, resulting in reduced switching losses. The paper discusses the analytical details of the approach using a simple DC-DC boost-buck converter and extends it to a DC to three-phase AC converter using the principles of space vector modulation. The paper presents detailed simulation and comparative results in terms of voltage over-shoots over long cables, loss calculations and electromagnetic noise. Results from a laboratory-scale working prototype confirm the benefits of the proposed approach in terms of EMI and loss reduction

Multi-task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs

Many popular knowledge graphs such as Freebase, YAGO or DBPedia maintain a list of non-discrete attributes for each entity. Intuitively, these attributes such as height, price or population count are able to richly characterize entities in knowledge graphs. This additional source of information may help to alleviate the inherent sparsity and incompleteness problem that are prevalent in knowledge graphs. Unfortunately, many state-of-the-art relational learning models ignore this information due to the challenging nature of dealing with non-discrete data types in the inherently binary-natured knowledge graphs. In this paper, we propose a novel multi-task neural network approach for both encoding and prediction of non-discrete attribute information in a relational setting. Specifically, we train a neural network for triplet prediction along with a separate network for attribute value regression. Via multi-task learning, we are able to learn representations of entities, relations and attributes that encode information about both tasks. Moreover, such attributes are not only central to many predictive tasks as an information source but also as a prediction target. Therefore, models that are able to encode, incorporate and predict such information in a relational learning context are highly attractive as well. We show that our approach outperforms many state-of-the-art methods for the tasks of relational triplet classification and attribute value prediction.Comment: Accepted at CIKM 201

Distributional analysis of entities

Arguably, one of the most important aspects of natural language processing is natural language understanding which relies heavily on lexical knowledge. In computational linguistics, modelling lexical knowledge through distributional semantics has gained considerable popularity. However, the modelling is largely restricted to generic lexical categories (typically common nouns, adjectives, etc.) which are associated with coarse-grained information i.e., the category country has a boundary, rivers and gold deposits. Comparatively, less attention has been paid towards modelling entities which, on the other hand, are associated with fine-grained real-world information, for instance: the entity Germany has precise properties such as, (GDP - 3.6 trillion Euros), (GDP per capita - 44.5 thousand Euros) and (Continent - Europe). The lack of focus on entities and the inherent latency of information in distributional representations warrants greater efforts towards modelling entity related phenomena and, increasing the understanding about the information encoded within distributional representations. This work makes two contributions in that direction: (a) We introduce a semantic relation – Instantiation, a relation between entities and their categories, and distributionally model it to investigate the hypothesis that distributional distinctions do exist in modelling entities versus modelling categories within a semantic space. Our results show that in a semantic space: 1) entities and categories are quite distinct with respect to their distributional behaviour, geometry and linguistic properties; 2) Instantiation relation is recoverable by distributional models; and, 3) for lexical relational modelling purposes, categories are better represented by the centroids of their entities instead of their distributional representations constructed directly from corpora. (b) We also investigate the potential and limitations of distributional semantics for the purpose of Knowledge Base Completion, starting with the hypothesis that fine-grained knowledge is encoded in distributional representations of entities during their meaning construction. We show that: 1) fine-grained information of entities is encoded in distributional representations and can be extracted by simple data-driven supervised models as attribute-value pairs; 2) the models can predict the entire range of fine-grained attributes, as seen in a knowledge base, in one go; and, 3) a crucial factor in determining success in extracting this type of information is contextual support i.e., the extent of contextual information captured by a distributional model during meaning construction. Overall, this thesis takes a step towards increasing the understanding about entity meaning representations in a distributional setup, with respect to their modelling and the extent of knowledge inclusion during their meaning construction

Critical analysis of Manasika Bhava in Anurjatajanya (allergic) diseases - A Survey Study

People are just as happy as they make up their minds to be. The state of mind defines ones healthy or unhealthy status. Human body and mind are always interlinked for the existence of life. Any alteration in functioning of body or mind gives formal invitation for various disorders. Ayurveda treatise has inculcated the importance of Manasika Bhava like Krodha (anger), Shoka (grief), Bhaya (fear) etc. at every influential place. In the present survey study, the inevitability of Manasika Bhava in Anurjatajanya (allergic) diseases is verified with the generated evidences. The World Health Organization (WHO) estimates 300 million individuals have asthma worldwide, a figure that could increase to 400 million by 2025 if trends continue. This is paralleled with a rising prevalence of skin allergies along with life threatening allergies like food allergies, drug allergies and anaphylaxis and more complex forms. This survey study end up with the result that, over 96% of the total registered allergic patients were with one or more abnormal Manasika Bhava

A System Dynamics Approach to SME Resilience Under the Economic Stress of the COVID-19 Pandemic: A Conceptual Model and Empirical Analysis

Thomas Peschken-Holt - ORCID: 0000-0002-1546-7258 https://orcid.org/0000-0002-1546-7258The COVID-19 pandemic reshapes our knowledge and reconceptualises our belief in small and medium enterprises (SMEs) as more flexible and resilient than bigger organisations under difficult socioeconomic conditions. The critical issue raised during the Covid-19 pandemic suggests that SMEs have confronted great challenges, which are not just concerned about SME survival, but also the paucity of strategic approach for firm revival and resilience. In dealing with the challenges, and based on a set of investigations, firstly, the authors provide a critically insightful review of the UK government Covid-19 responses based on four themes of UK government interventions. Secondly, they offer contextual evidence based on their analysis of SME performance in relation to the government responding schemes and how that affects SME operations in the UK. Thirdly, they propose a framework with tentative strategic solutions based on both theoretical reviews and the empirical analysis for how SMEs revive and become resilient.https://doi.org/10.4018/IJAMTR.3022424pubpub

A comparative study of effect of fluoroquinolones on blood glucose levels in rats

Background: Fluoroquinolones (FQ) are frequently prescribed because of their broad-spectrum applicability in treatment of community acquired pneumonia and urinary tract infections. Increased use has raised some concerns regarding side effects like dysglycaemia, tendon rupture and QT interval prolongation. Gatifloxacin was banned in India in 2011 for causing fatal hypoglycemia. This study compares the effect of different fluoroquinolones on blood glucose levels in rats.Methods: 24 rats were divided into four study groups. Each group was administered one fluoroquinolone namely levofloxacin 9 mg, moxifloxacin 7.2 mg, ciprofloxacin 18 mg and ofloxacin 14.4 mg respectively for five days. The changes in blood glucose levels were observed for 10 days.Results: The mean blood glucose levels in all the four groups dropped below the baseline by day five. A statistically significant reduction in mean glucose levels was found in the moxifloxacin, ciprofloxacin and levofloxacin group. Among the rats that were given ofloxacin, the reduction in the mean blood glucose levels was not statistically significant. After stopping the drugs, the blood glucose levels in all the four groups returned near to the baseline within five days.Conclusions: The use of fluoroquinolones causes hypoglycemia in rats. The blood glucose level reduction associated with moxifloxacin was maximum, whereas ofloxacin appeared to have the minimum effect on blood glucose levels. These effects do not appear to be permanent and the dysglycaemia subsided after the drugs were stopped

A Study on the Constraints faced by the Vegetable Growers in Mallawan Block of Hardoi District in Uttar Pradesh, India

Training is the analytical process for human resource development as well as also playsan essential role in hastening of particular attitude among human behaviours. Moreover, training is a tool for accomplishingintervention of thelevel of human resource, that becomesincreasingly and pivotalfor the development substantially in all fields with an extending satisfaction in technology. The present investigation was aimed to observe the constraints faced by the vegetable growers in the production of vegetable cultivation. The data of constraints were assembled and analysed by using Garret’s ranking methodology to develop a quantitative position of each constraint. Likewise, data findings were showed that thevegetable growersfacedtechnological, resource, market and miscellaneous constraints. Among technological constraints, lack of knowledge about improved varieties, seed rate including sowing time were prominent. Similarly,regarding resource constraints, high costs of pesticides were more dominant. Furthermore, in case of market constraints, non- remunerative price and poor marketing facilities results, high risk which wasdominant. And last one is miscellaneous constraint, non-availability of labour during peak season and high wages. Therefore, numerous field level constraints faced by the vegetable growers had been recognized as well as categorized into the circumstances for training needs

Functional dissection of the catalytic carboxyl-terminal domain of Origin Recognition Complex Subunit 1 (PfORC1) of the human malaria parasite Plasmodium falciparum

Origin Recognition Complex subunit 1 (ORC1) is essential for DNA replication in eukaryotes. The deadly human malaria parasite Plasmodium falciparum contains an ORC1/CDC6 homolog with several interesting domains at the catalytic carboxyl-terminal region that include a putative nucleoside triphosphate-binding and hydrolysis domain, a putative PCNA-Interacting-Protein (PIP) motif and an extreme C-terminal region that shows poor homology with other ORC1 homologs. Due to the unavailability of a dependable inducible gene expression system, it is difficult to study the structure and function of essential genes in Plasmodium. Using a genetic yeast complementation system and biochemical experiments, here we show that the putative PIP domain in ORC1 that facilitates in vitro physical interaction with PCNA is functional in both yeast (Saccharomyces cerevisiae) and Plasmodium in vivo, confirming its essential biological role in eukaryotes. Furthermore, despite having less sequence homology, the extreme C-terminal region can be swapped between S. cerevisiae and P. falciparum and it binds to DNA directly, suggesting a conserved role of this region in DNA replication. These results not only provide us a useful system to study the function of the essential genes in Plasmodium, they help us to identify the previously undiscovered unique features of replication proteins in general