Prostate cancer clinical trials of the Southwest Oncology Group

The changing clinical dynamics of prostate cancer have resulted in a broadening of the research focus of the Genitourinary (GU) Cancer Committee of the Southwest Oncology Group (SWOG). Beginning with an emphasis on hormone-refractory disease in its early years, SWOG prostate cancer trials now cover the entire spectrum of the disease: localized, locally advanced, metastatic and hormone-refractory disease. As the world's largest GU cancer research group, the GU committee of SWOG has pioneered studies in combined androgen therapy for metastatic disease, quality-of-life (QOL) assessments for patients with localized and advanced disease, adjuvant therapy models, and prostate cancer chemoprevention. The committee has also formed the GU Global Group, whose purpose is to convene the chairs of the GU committees of all the major national and international oncology cooperative groups. Meeting semiannually, this group discusses activities within their respective organizations, plans collaborative strategies and protocols, and establishes global strategy in prostate cancer clinical research. The future directions of national and international prostate cancer trials will build on this broad foundation of well-conceived, logically sequenced studies.     

Principles of high-resolution radar based on nonsinusoidal waves.II. Generalized ambiguity function

For pt.I see ibid., vol.31, no.4, p.359-68 (1989). A generalized ambiguity function for nonsinusoidal coded waveforms that are represented as a train of properly weighted Gaussian pulses is derived and analyzed to study its properties. Computer plots of generalized ambiguity functions of different binary codes, e.g. Barker, complementary, and pseudorandom codes, are presented. The various computer plots show that the desired thumbtack ambiguity function is achievable by nonsinusoidal coded waveforms     

A comparison between reliability analyses based primarily on disjointness or statistical independence: The case of the generalized indra network

A switching expression is readily convertible to a reliability expression if (a) all ORed terms are disjoint, and (b) all ANDed sums are statistically independent. The usual approach of system reliability analysis makes a primary use of (a) and a secondary use of (b). An alternative approach reverses the roles of (a) and (b). Symbolic reliability expressions for the source-to-terminal reliability of a generalized Indra network (GIN) with nonidentical components are derived by the two approaches. For this particular case, the second approach leads to a shorter, more elegant derivation and simpler novel results. Typical plots of the GIN reliability functions are presented and their properties are discussed.     

Smart home security: challenges,issues and solutions at different IoT layers

The Journal of Supercomputing - The Internet of Things is a rapidly evolving technology in which interconnected computing devices and sensors share data over the network to decipher different...     

Hyperspectral imaging-based unsupervised adulterated red chili content transformation for classification: Identification of red chili adulterants

Neural Computing and Applications - Preserving red-chili quality is of utmost importance in which the authorities demand quality techniques to detect, classify, and prevent it from impurities. For...     

A hybrid model for spelling error detection and correction for Urdu language

Neural Computing and Applications - Detecting and correcting misspelled words in a written text are of great importance in many natural language processing applications. Errors can be broadly...     

Feature Selection and Extraction Methods for Power Systems Transient Stability Assessment Employing Computational Intelligence Techniques

One of the important aspects in achieving better performance for transient stability assessment (TSA) of power systems employing computational intelligence (CI) techniques is by incorporating feature reduction techniques. For small power system the number of features may be small but when larger systems are considered the number of features increased as the size of the systems increases. Apart from employing faster CI techniques to achieve faster and accurate TSA of power system, feature reduction techniques are needed in reducing the input features while preserving the needed information so as to make faster training of the CI technique. This paper presents feature reductions techniques used, namely correlation analysis and principle component analysis, in reducing number of input features presented to two CI techniques for TSA, namely probabilistic neural network (PNN) and least squares support vector machines (LS-SVM). The proposed feature reduction techniques are implemented and tested on the IEEE 39-bus test system and 87-bus Malaysia’s power system. Numerical results are presented to demonstrate the performance of the feature reduction techniques and its effects on the accuracies and time taken for training the two CI techniques.     

RFID-based interactive multimedia system for the children

In this paper, we present an interactive edutainment system for the children that leverages multimedia and RFID technologies in a seamless manner. The proposed system allows children to learn about new objects/entities by tapping on physical objects through a specially designed RFID-Bluetooth based Tangible User Interface (TUI) tool. The output of the system is delivered as a set of appropriate multimedia representations related to the objects being tapped. The TUI uses RFID technology for object identification and Bluetooth communication to transmit data to the computer where the system??s software is running. We incorporated our system in three games that allow children of different ages to benefit from the system??s functionalities and encourage them to interact with it.     

Sentic Computing for social media marketing

In a world in which millions of people express their opinions about commercial products in blogs, wikis, fora, chats and social networks, the distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand or organization. Opinion mining for product positioning, in fact, is getting a more and more popular research field but the extraction of useful information from social media is not a simple task. In this work we merge AI and Semantic Web techniques to extract, encode and represent this unstructured information. In particular, we use Sentic Computing, a multi-disciplinary approach to opinion mining and sentiment analysis, to semantically and affectively analyze text and encode results in a semantic aware format according to different web ontologies. Eventually we represent this information as an interconnected knowledge base which is browsable through a multi-faceted classification website.     

Autonomous mapping of HL7 RIM and relational database schema

Healthcare systems need to share information within and across the boundaries in order to provide better care to the patients. For this purpose, they take advantage of the full potential of current state of the art in healthcare standards providing interoperable solutions. HL7 V3 specification is an international message exchange and interoperability standard. HL7 V3 messages exchanged between healthcare applications are ultimately recorded into local healthcare databases, mostly in relational databases. In order to bring these relational databases in compliance with HL7, mappings between HL7 RIM (Reference Information Model) and relational database schema are required. Currently, RIM and database mapping is largely performed manually, therefore it is tedious, time consuming, error prone and expensive process. It is a challenging task to determine all correspondences between RIM and schema automatically because of extreme heterogeneity issues in healthcare databases. To reduce the amount of manual efforts as much as possible, autonomous mapping approaches are required. This paper proposes a technique that addresses the aforementioned mapping issue and aligns healthcare databases to HL7 V3 RIM specifications. Furthermore, the proposed technique has been implemented as a working application and tested on real world healthcare systems. The application loads the target healthcare schema and then identifies the most appropriate match for tables and the associated fields in the schema by using domain knowledge and the matching rules defined in the Mapping Knowledge Repository. These rules are designed to handle the complexity of semantics found in healthcare databases. The GUI allows users to view and edit/re-map the correspondences. Once all the mappings are defined, the application generates Mapping Specification, which contains all the mapping information i.e. database tables and fields with associated RIM classes and attributes. In order to enable the transactions, the application is facilitated with the autonomous code generation from the Mapping Specification. The Code Generator component focuses primarily on generating custom classes and hibernate mapping files against the runtime system to retrieve and parse the data from the data source—thus allows bi-directional HL7 to database communication, with minimum programming required. Our experimental results show 35–65% accuracy on real laboratory systems, thus demonstrating the promise of the approach. The proposed scheme is an effective step in bringing the clinical databases in compliance with RIM, providing ease and flexibility.