Analysis of Voltage Sag in Sub Transmission System

The disturbances created in A C Transmission line power flow due to various faults and unavoidable natural circumstances has to be monitored carefully for uninterrupted power supply to the consumer and to avoid hazardous situations. The subtransmission system with standard 33kv, 66kv, 132kv and 220kv transmission lines which are usually out of sight are considered for analysis purpose. The various faults which occur in transmission lines leading to voltage sag are simulated, modelled and analyzed using MATLAB/SIMULINK tool

An overview Survey on Various Video compressions and its importance

With the rise of digital computing and visual data processing, the need for storage and transmission of video data became prevalent. Storage and transmission of uncompressed raw visual data is not a good practice, because it requires a large storage space and great bandwidth. Video compression algorithms can compress this raw visual data or video into smaller files with a little sacrifice on the quality. This paper an overview and comparison of standard efforts on video compression algorithm of: MPEG-1, MPEG-2, MPEG-4, MPEG-

Genetic determinants of response and adverse effects following vitamin K antagonist oral anticoagulants

Background: Vitamin K antagonist anticoagulants (warfarin/acenocoumarol) are commonly used anticoagulants that require careful clinical management to balance the risks of over anticoagulation and bleeding with those of under anticoagulation and clotting. Genetic variants of the enzyme that metabolizes vitamin K antagonist anticoagulant, cytochrome P-450 2C9 (CYP2C9), and of a key pharmacologic target of vitamin K antagonists anticoagulant, vitamin K epoxide reductase (VKORC1), contribute to differences in patients' responses to various anticoagulant doses. Methods: In thirty patients on oral vitamin K antagonist anticoagulant therapy, presented with either clotting manifestations (valve thrombosis, pulmonary embolism and DVT) or prolonged INR/bleeding manifestations, we assessed CYP2C9 genotypes, VKORC1 haplotypes, clinical characteristics, response to therapy (as determined by the international normalized ratio [INR]), and bleeding events.Results: Of the thirty patients, thirteen patients INR was high and four patients presented with major bleeding and four with minor bleeding manifestations. Out of thirteen patients with high INR, ten patients showed CYP2C9 polymorphism (*1/*3 and *2/*3) of poor metabolizer genotype. Most of the high INR patients were recently started on oral vitamin K antagonist anticoagulant. Most patients presented with clotting manifestations with below therapeutic INR are noncompliant with anticoagulants.Conclusions: The results of this study suggest that the CYP2C9 polymorphisms are associated with an increased risk of over anticoagulation and of bleeding events among patients on vitamin K antagonists’ anticoagulant setting.  Screening for CYP2C9 variants may allow clinicians to develop dosing protocols and surveillance techniques to reduce the risk of adverse drug reactions in patients receiving vitamin K antagonist anticoagulants. However the cost-effectiveness of genotyping of patients must be considered.                

Vishaghna (anti-toxic) property of Shirisha (Albizia lebbeck) : A Review

Ayurveda is a traditional healthcare system of Indian medicine since ancient times. Majority of medicine mentioned in Ayurveda are plant based. Herbal medicine is based on the premise that plants and plants extracts contain natural phytochemicals with biological activity that can promote health or alleviate illness. Shirisha (Albizia lebbeck) is one of the important herbs having broad spectrum therapeutic effects. In classical textbook it is mentioned as the best among the Vishaghna (anti poisonous) drug. In Ayurveda it is used in allergic skin conditions, allergic cough and seasonal cold to get relief. It’s action is Shothara (anti-inflammatory), Vedanasthapan (analgesic), Varnya (complexion promoter), Vrishya (Spermatogogue), Vishaghna (antipoisonous), Shirovirechana (Nasya), Chakshushya (beneficial to eyes), Stambhana (anti Diarrheal), Kaphagna (antitussive), Raktashodhaka (Blood purifier) and Kustaghna (anti leprotic), Kandughna (Antipruritic). Research conducted during past have also reported its anti-inflammatory, anti-histaminic, antianaphylactic, anti-asthmatic, anti-microbial properties

Constructing an Office Domain Ontology using Knowledge Engineering Process

Knowledge based identification of human activities in systems depends primarily on rich contextual domain knowledge casing all of the information about the human, objects around human and also relations amongst them. Knowledge engineering plays an important role in building knowledge based expert systems, to solve complex problems such as human activity recognition. This requires formal representation of the knowledge which is based on the conceptualization of the domain. Ontology is awidely chosen representational model that depicts knowledge as a set of concepts. In this work, we have applied knowledge engineering process for constructingthe domain ontology of the officeenvironment in agreement with the ontology development life cycle

Comparative clinical study of Nasya Karma and Shirodhara with Prapaundarikadi Taila in Ardhavabhedaka w.s.r. to Migraine

Background: Ardhavabhedaka is a type of Shiroroga with the cardinal feature of unilateral headache, which if left untreated leads to complications like blindness and hearing loss. This disease can be correlated to Migraine head-ache based on the clinical manifestations. Nasya Karma and Shirodhara are the prime treatment modalities for Shirorogas. Objectives: To evaluate the effects of Nasyakarma and Shirodhara in the management of Ardhavabhedhaka. Material and Methods: Patients presenting with the classical features of Ardhavabhedaka and between the age group of 18 to 60 years irrespective of sex were selected and allotted in Group A and B with 20 patients in each group. Group A was administered with Nasya with Prapaundarikadi Taila and Group B with Shirodhara with Prapaundarikadi Taila for 7 days. Result: Data was tabulated and analyzed using Student t-test, paired proportion test, which showed marked improvement in patients with Ardhavabhedaka in both the groups. Nasya and Shirodhara with Prapaundarikadi Taila is proved effective in all patients. According to percentage wise relief in the symptoms of Ardhavabhedaka in Group A and B, Group A showed comparatively better relief. Conclusion: On the basis of the results of this study, it can be clearly concluded that Nasya performed with Prapaundarikadi Taila provided significant relief in the signs and symptoms of Ardhavabhedaka than Shirodhara performed with Prapaundarikadi Taila

Flexible Deep Learning in Edge Computing for Internet of Things

Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing environment. Traditional edge computing models have rigid characteristics. Flexible edge computing architecture solves rigidity in IoT edge computing. Proposed model combines deep learning into edge computing and flexible edge computing architecture using multiple agents. Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing. FEC architecture is a flexible and advanced IoT system model characterized by environment adaptation ability and user orientation ability. In the performance evaluation, we test the performance of executing deep learning tasks in FEC architecture for edge computing environment. The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT

Flexible Deep Learning in Edge Computing for Internet of Things

Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing environment. Traditional edge computing models have rigid characteristics. Flexible edge computing architecture solves rigidity in IoT edge computing. Proposed model combines deep learning into edge computing and flexible edge computing architecture using multiple agents. Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing. FEC architecture is a flexible and advanced IoT system model characterized by environment adaptation ability and user orientation ability. In the performance evaluation, we test the performance of executing deep learning tasks in FEC architecture for edge computing environment. The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT