Miracle-dz/UniFAR-FTUs huggingface dataset:
A Voltage-Boosting Submodules Based Modular Multilevel Converter With Temporary Energy Storage Ability for Fault Ride Through of Offshore Wind VSC-HVDC System

VSC-HVDC technology has broad application prospects in the field of long-distance and large-capacity offshore wind system, and it is of great significance to realize the fault ride through (FRT) of the system. Aiming at the problems of high cost of adding DC chopper and low reliability of actively reducing wind turbine power, the scheme of temporary improving MMC capacitor voltage to store energy to realize FRT is considered to have the advantages of low cost and high reliability. However, due to the limit of insulated gate bipolar transistor (IGBT) operating voltage, the existing method do not have strong fault ride through ability. To solve this problem, the voltage-utilization boosting circuit composed of gap and metal oxide varistor (MOV) is proposed to ensures the safe operation of the IGBT after capacitor voltage temporary rise. In addition, a fault ride through strategy based on the cooperation of wind turbine and the voltage-boosting submodules (SMs) based modular multilevel converter with temporary energy storage ability (VBS-MMC) is proposed in this paper. Through the organic combination of new nonlinear circuit and control strategy, the system FRT under various fault conditions is realized. Furthermore, a 3.3 kV welded IGBT experimental platform is built, and the results show that the SM temporary voltage-boosting method is feasible. Finally, the simulation results of ± 320 kV/400 MW VSC-HVDC system verify the FRT capability of VBS-MMC. And the cost and performance comparisons are analysis.
Contemporary Affirmation of Database Indexing Strategies

Here in this paper we present an orderly comparison of the applications that by using flat file and a diversity of indexed database approaches. In this regard we consider the set of data mining approaches as applications. Measurements of run time as a function of database and minimum support threshold are examined. Experimental profiling measures of the frequency and cost of a variety of operations are discussed. This analysis motivated both the use of adaptive ARM techniques and the development of a simple yet novel linked block structure to support efficient transaction pruning. We also explore techniques for determining what kinds of data benefit from pruning, and when pruning is likely to help.
A Securing And Sharing Data Less Cost-Effective Using Multicloud Storage I Priyadharsshini.C, II SathishKumar.S, III Ranjidha.P

While using cloud services they impose new security challenges, which is the biggest obstacle. Lot of research activities has been triggered in this direction, which results in a quantity of proposals targeting the various security threats. Simultaneous usage of multiple Clouds reduce the risk for data and applications in a public cloud . Recently several approaches employing this paradigm have been proposed . They differ in partitioning and distribution patterns, technologies, cryptographic methods, and targeted scenarios as well as security levels. These developed multi cloud architectures allow to categorize the available schemes and to analyze them. An assessment of the different methods with regards to legal aspects and compliance implications is given in particular. Besides the security issues , it also provide a new set of unique features which open the path towards novel security approaches, techniques and architectures. This project initiates this discussion by contributing a concept which achieves security merits by making use of multiple distinct clouds at the same time.
Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

: Data Clustering: Algorithms and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) ISBN : #1466558210 | Date : 2013-08-21 Description : PDF-7caf7 | Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention t. Data Clustering: Algorithms and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
Redetermination of CaB8O11(OH)4 at low temperature

The structure of CaB8O11(OH)4 (calcium octa­borate tetra­hydroxide) [Zayakina & Brovkin (1978). Kristallografiya, 23, 1167–1170] has been redetermined at 120 (2) K with improved precision. The O—H⋯O hydrogen-bonding arrangement has been established, based on freely refined H-atom positions.
Exploiting individual users and user groups interaction features: methodology and infrastructure design

The user may be a source of evidence for supporting information access through Digital Library (DL) systems. In particular, the features gathered while monitoring the interaction between the user and a DL system can be used as implicit indicators of the user interests. However, each user has his own style of interaction and a feature which is a reliable indicator with regard to one user may be no longer reliable when referred to another user. This suggests the need to develop personalized approaches for each user which are tailored for each search task. Nevertheless, the behavior of a group of interrelated users, e.g. performing the same task, may improve the contribution provided by the personal behavior; for instance, some interaction features, if considered individually, are more reliable with regard to a group of users. This paper introduces a methodology for exploiting both the behavior of individual users and group of users as sources of evidence. The paper also introduces a software infrastructure implementing the methodology. The methodology is mainly based on a geometric framework while the software infrastructure is based on a partially decentralized Peer-To-Peer (P2P) network, thus permitting the management of different sources of evidence.
Effectiveness of Multidisciplinary Intervention in Obese Women with Binge Eating Disorder

Binge eating disorder (BED) is a psychiatric disorder characterized by eating large amounts of food, not followed by inappropriate compensatory behaviors for weight control that characterize other eating disorders. In 2013, it was approved, by the American Psychiatric Association, the inclusion of this disorder in the diagnostic and statistical manual of mental disorders 5th edition (DSM-V) as its own category of eating disorder.1 There is an important relationship between this disorder and obesity, which complexity is linked to psychological and physical implications, such as a low self-esteem, weight and shape concerns, and unhealthy eating behaviors including binge eating.2
Quality of life of women with breast cancer undergoing chemotherapy

Objective: to analyze the quality of life of women with breast cancer undergoing chemotherapy. Methods: a cross-sectional study with 76 women. We used the European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire-30 and Supplementary Questionnaire Breast Cancer Module. Results: there was a low correlation between socio-demographic and clinical variables with the quality of life domains. The best scores corresponded to women from 49 to 51 years old, married, dark-skinned, better schooling and formal work. The least mentioned symptoms were: Swollen breast (84.2%), Vomiting (81.6%); Shortness of breath (80.3%). Conclusion: general health presented satisfactory average, which suggests a good quality of life according to the European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire-30. However, the Functional and Symptoms domains, as measured by Supplementary Questionnaire Breast Cancer Module, were negatively influenced by old age and larger cycles of chemotherapy.
Timelywsn: middleware architecture for probabilistic support in WSNs

Recently, there has been a lot of interest and research in the field of wireless sensor networks (WSNs). Wireless ad-hoc networks made of hundreds to thousands of computer nodes equipped with sensory equipment, that measure the state of physical entities. WSNs can be used in numerous scenarios and applications, for instance, in forest fire prevention and detection, industrial monitoring, or patient-health monitoring. Many of these applications will have real-time requirements. However, WSNs present some unique issues, that make the provisioning of hard real-time guarantees an unreasonable objective. Hard real-time systems are deterministic and predictable, while WSNs are, by nature, unpredictable. Alternatively, some researchers have explored the provisioning of some Quality of Service (QoS), as an alternative to hard real-time guarantees. This thesis describes the study and definition of a middleware architecture for the support of applications with real-time requirements in WSNs, built on the concepts of probabilistic QoS guarantees, statistical monitoring, and application adaptation. It identifies a set of underlying services that need to be implemented as part of this framework, explaining why they are needed and what they provide. Furthermore This thesis presents, Probabilistic Latency Estimation based Routing (PLER), a routing protocol for WSNs, that takes advantage of the middleware services, serving as a proof-of-concept application.
Contour detection of hippocampus using dynamic contour model and region growing

In hippocampal morphology, abnormalities including unilateral or bilateral volume loss are known to cause epilepsy, Alzheimer's disease, and certain amnestic syndromes. The accurate segmentation of the hippocampal contour is critical for the diagnosis. However, it is very difficult to extract the contour that matches exactly the hippocampal region in magnetic resonance images (MRI). The brightness of the hippocampal region varies linearly and it's size is small. For the accurate and consistent detection of the hippocampal region, a method which combines region growing and a dynamic contour model to detect the hippocampus from MRI brain images is presented. The segmentation process is performed in two steps. First, region growing with a seed point is performed in the hippocampal region and its output is used for the initial contour of the dynamic contour model. Second, the initial contour is modified on the basis of criteria which integrate energy with contour smoothness and image gradient along the contour. As a result, this method improves the sensitivity of the choice of the initial seed point and precisely extracts the hippocampus from the MRI brain image.
Emerging potential of cell based therapies for articular cartilage repair and regeneration

Osteoarthritis is a form of arthritis described as a degenerative joint disease that results from the breakdown of articular cartilage and underlying subchondral bone [1,2]. Cartilage is firm, rubbery, protein substance that serves as a “cushion” between the bones, providing a smooth, gliding surface for joint motion. As the cartilage breaks down, it causes pain, swelling, stiffness and problems moving the joint [3]. Therefore, Osteoarthritis is a chronic disease and a major cause of disability, characterized by irreversible cartilage degeneration, bony changes of the joints, deterioration of tendons and ligaments, and inflammation of the synoviumas shown in Figure 1 [4]. An initial degeneration, known as the ‘pre-OA’ state may be reversible spontaneously or through preventive measures, however, if not stopped, the disease progresses to later stages which results in loss of articular cartilage surface integrity, break down of bone and osteophyte formation [3,5]. OA can affect any joint, but it most often occurs in the knee, hip, lower back and neck, small joints in the finger and base of the big toe (Figure 2)[6,7].
IMAGE PROCESSING FOR MEDICAL EVALUATION ON NBI ENDOSCOPY IMAGES

The employment of digital images has grown in recent years in several areas. Mainly due by technological and computer development, its use has a larger amount of avail- able information in applications such as: security, navigation and medicine. In most applica- tions, the use of image processing techniques for automatic information extraction has been needed to help experts and professionals in several areas. At this paper is presented an appli- cation of image processing and computer vision techniques with images obtained by the new NBI (Narrow Band Imaging) equipment, an endoscopic technique that has been used to en- hance the accuracy of diagnosis by using narrow-bandwidth filters in a red-green-blue (R/G/B) sequential illumination system. Several techniques for image processing have been employed in this work to highlight some lesions in order to assist diagnosis of flat lesions found in intestines or stomach.
Modeling of Fluid Damping in Resonant Micromirrors with Out-of-Plane Comb-Drive Actuation

Comb-drive micromirrors are becoming of interest for a broad range of light manipulation applications. For some of these applications a vacuum packaging, which is able to guarantee higher quality factors, turns out to be not favorable for technical and functional reasons. Furthermore, micromirrors for picoprojectors application are required to function at high frequencies in order to achieve high resolution images. Accordingly, a study of the energy dissipated due to the interaction between the moving parts of the micromirror and the surrounding air, leading to fluid damping, is an important issue. Even if air damping has been thoroughly studied, an extension to large air domain distortion linked to large tilting angles of torsional micromirrors, is still partially missing. In such situations, the flow formation turns out to be far more complex than that assumed in analytical models. This task is here pursued by adopting three-dimensional computational fluid dynamics models; specifically, two models, holding at different length scales, are adopted to attack the problem through an automated dynamic remeshing method. The time evolution of the torque required to compensate for the fluid damping term is computed for a specific micromirror geometry. The relative contributions due to drag, shear and squeeze film terms are calculated as well. These results are shown to compare well with the available experimental data in terms of the overall sensor quality factor.
Design and Clinical Evaluation of a Novel Low-Glucose Prediction Algorithm with Mini-Dose Stable Glucagon Delivery in Post-Bariatric Hypoglycemia.

BACKGROUND Postbariatric hypoglycemia (PBH) is a complication of bariatric surgery with limited therapeutic options. We developed an event-based system to predict and detect hypoglycemia based on continuous glucose monitor (CGM) data and recommend delivery of minidose liquid glucagon. METHODS We performed an iterative development clinical study employing a novel glucagon delivery system: a Dexcom CGM connected to a Windows tablet running a hypoglycemia prediction algorithm and an Omnipod pump filled with an investigational stable liquid glucagon formulation. Meal tolerance testing was performed in seven participants with PBH and history of neuroglycopenia. Glucagon was administered when hypoglycemia was predicted. Primary outcome measures included the safety and feasibility of this system to predict and prevent severe hypoglycemia. Secondary outcomes included hypoglycemia prediction by the prediction algorithm, minimization of time below hypoglycemia threshold using glucagon, and prevention of rebound hyperglycemia. RESULTS The hypoglycemia prediction algorithm alerted for impending hypoglycemia in the postmeal state, prompting delivery of glucagon (150 μg). After observations of initial incomplete efficacy to prevent hypoglycemia in the first two participants, system modifications were implemented: addition of PBH-specific detection algorithm, increased glucagon dose (300 μg), and a second glucagon dose if needed. These modifications, together with rescue carbohydrates provided to some participants, contributed to progressive improvements in glucose time above the hypoglycemia threshold (75 mg/dL). CONCLUSIONS Preliminary results indicate that our event-based automatic monitoring algorithm successfully predicted likely hypoglycemia. Minidose glucagon therapy was well tolerated, without prolonged or severe hypoglycemia, and without rebound hyperglycemia.
General-Weighted Least-Norm Control for Redundant Manipulators under Time-Dependent Constraint

Previous research dealing with constrained kinematic redundancy problems focuses on manipulators with time-independent constraints. This paper extends the general-weighted least-norm (GWLN) method to manipulators with time-dependent constraints by introducing time-dependent virtual joints. In the virtual joint space, corresponding task space velocity is revised to encapsulate the effects of time-dependent parameters of constraints. This is done so that an intermediate kinematic control problem with only joint limit is obtained. Then, the inverse kinematic problem is solved in the virtual joint space. A new inverse-weighted matrix setting criterion is proposed to replace the one-step prediction that originally complicated implementation of the GWLN method. To demonstrate the efficacy of the method, simulations and experiments are carried out on a five-link welding manipulator to track the welding trajectory. Time-dependent orientation constraint and preventing joint limits is guaranteed using this method.
A fuzzy neural dynamics based tracking controller for a nonholonomic mobile robot

In this paper, a fuzzy neural dynamics based tracking controller for nonholonomic wheeled mobile robots is proposed. The nonholonomic kinematic constraints are considered in the development of the controller. The proposed model is suitable for both continuous and discrete paths. Fuzzy rules are formulated to deal with the discontinuity in path directions. This model is capable of generating smooth velocity commands to drive the robot to track the desired paths. In the situation with large initial errors, the proposed model can automatically generate a smooth curve to reach the desired robot path from an arbitrary initial configuration without any explicit algorithms for the connection curve. At sharp turns, this model can automatically round off the sharp turns with a smooth curve. The effectiveness of the proposed tracking controller is demonstrated by simulation studies.
Impact of radio resource control mechanisms on the performance of Internet applications

The future generations of wireless networks are being designed to provide various types of packet data services. Radio resource control (RRC) protocols should be designed to configure the radio bearer so that the radio system capacity is maximized, the mobile terminal's battery drain is minimized and users' traffic requirements are met. It is inevitable that the RRC protocol will affect the performance of the network transport protocols and, in return, the network transport protocols will limit the radio systems ability to schedule resources efficiently. In this paper we describe a genetic model of RRC protocol states, which represents various air interfaces. Using this model, we investigate how the parameters of RRC protocol states affect the radio system control overhead and performance of Web browsing applications.
Mimosa pudica (Lajwanti)Accelerates Repair and Regeneration of Deep Dermal Excision Wound in Swiss Albino Mice

The traditional medicine has originated with the infliction of diseases in the human beings as the natural products including plants were in the easy reach of humans. The humans have depended on plants and other natural products for their healthcare since time immemorial. The herbs and natural products continue to play a major role to treat several diseases even during the modern era. According to an estimate by World Health Organization 80% of the world’s population including developed and developing countries use plants and natural products for their healthcare [1]. The pharmacopeias were dominated by use of plants and natural product 200 years ago. However, the use of plants as medicine declined with the establishment of pharmacology in the therapeutics and development of several chemical synthetic drugs [2]. Despite the availability of modern medical system the majority of the word population relies on medicinal plants because plants have been tested for several generations for their use as medicine to treat different ailments and the use of medicinal plants has been considered non-toxic at the dosage that are administered for the treatment of different ailments. This may be because of their biologic origin in contrast to modern synthetic drugs, which are toxic even at their effective dosages. The importance of medicinal plant can be realized by the fact that usage of herbal medicine has been constantly increasing in the general population of USA and its use increased as much as 380% from a mere 2.5 to 12.1% between 1990 1997 [3].
An efficient distributed channel management algorithm for cellular mobile networks

We propose a novel channel management algorithm, called distributed load balancing with selective borrowing (D-LBSB), for cellular mobile networks. As an underlying approach, we start with a fixed channel assignment scheme where each cell is initially allocated a set of local channels, each to be assigned on demand to a user in that cell. The novelty of our D-LBSB scheme lies in handling the hot cell problem because it proposes to migrate unused channels from suitable cold cells to the hot ones through a distributed channel borrowing algorithm. With the help of a Markov model, the probability of a cell being hot and the call blocking probability in a cell are derived. Detailed simulation experiments are carried out in order to evaluate our proposed methodology. Performance comparison reveals that the D-LBSB scheme performs better than a centralized version in an overloaded system, and significantly better than several other existing schemes in terms of call blocking probability, under moderate and heavy loads.
Evaluating the impact of peer review and participation awareness in an online collaborative document authoring environment

Online Learning Environments (OLEs) have been widely adopted by higher education facilities, offering distance education with the potential to support the social and collaborative aspects deemed crucial to modern constructivist pedagogy. Groupware, a form of software which aims to facilitate group work, has been the subject of much research, from both educational and enterprise perspectives. This research introduced Reportal, an online groupware system designed to facilitate the collaborative authoring of a document. Reportal's peer review and participation awareness features were the focus of this research, and their impact was measured against the elements of online collaboration, a typology established by the author which reflects the factors that the literature has defined as being influential to effective online collaboration. The findings from a multiple case study of university students using the Reportal system indicate that peer review and participation awareness features in groupware have distinct positive impacts on the elements of online collaboration. While the research was a preliminary investigation, such findings are a first step in distinguishing, refining and implementing groupware features which support collaboration in an online environment. Implementation of such features has the potential to increase the effectiveness of online collaboration in education and enterprise significantly.