Real-time Search Research Articles

Multistate analysis of prospective Legionnaires' disease cluster detection using SaTScan, 2011-2015.

Detection of clusters of Legionnaires’ disease, a leading waterborne cause of pneumonia, is challenging. Clusters vary in size and scope, are associated with a diverse range of aerosol-producing devices, including exposures such as whirlpool spas and hotel water systems typically associated with travel, and can occur without an easily identified exposure source. Recently, jurisdictions have begun to use SaTScan spatio-temporal analysis software prospectively as part of routine cluster surveillance. We used data collected by the Active Bacterial Core surveillance platform to assess the ability of SaTScan to detect Legionnaires’ disease clusters. We found that SaTScan analysis using traditional surveillance data and geocoded residential addresses was unable to detect many common Legionnaires’ disease cluster types, such as those associated with travel or a prolonged time between cases. Additionally, signals from an analysis designed to simulate a real-time search for clusters did not align with clusters identified by traditional surveillance methods or a retrospective SaTScan analysis. A geospatial analysis platform better tailored to the unique characteristics of Legionnaires’ disease epidemiology would improve cluster detection and decrease time to public health action.

Design of FRB real-time search backend for multi-beam receiver

Fast radio burst (FRB) is an occasional and instantaneous radio burst. In order to detect more samples for research, this paper develops a real-time search digital backend based on multi-beam receiver. The system includes data acquisition and processing, single-beam signal processing, multi-beam signal synthesis and analysis, detection results storage and distribution. It can collect and analyze multi-beam signals simultaneously. Multi-beam signal reference effect is used to eliminate radio frequency interference and improve detection accuracy. Observation experiments were carried out on a radio telescope. The function of FRB detection was tested by capturing the single pulse signal of a pulsar. The result shows that the digital backend can achieve accurate and real-time FRB detection.

Rationality of inflation expectations: an interpretation of Google Trends data

ABSTRACTThis study derives inflation expectations from the internet search query data of Google Trends and validates the rationality criteria for India. The empirical analysis is carried out with proper consideration of persistence characteristics of the data for the period from January 2006 to April 2018. The results indicate the derived inflation expectations fulfil the properties of rationality. The results have far-reaching policy implications for the better conduct of monetary policy in favour of using real-time internet search data as an indicator of inflation expectations.

Applying Simulated Annealing and Parallel Computing to the Mobile Sequential Recommendation

We speed up the solution of the mobile sequential recommendation (MSR) problem that requires searching optimal routes for empty taxi cabs through mining massive taxi GPS data. We develop new methods that combine parallel computing and the simulated annealing with novel global and local searches. While existing approaches usually involve costly offline algorithms and methodical pruning of the search space, our new methods provide direct real-time search for the optimal route without the offline preprocessing. Our methods significantly reduce computational time for the high dimensional MSR problems from days to seconds based on the real-world data as well as the synthetic ones. We efficiently provide solutions to MSR problems with thousands of pick-up points without offline training, compared to the published record of 25 pick-up points.

Single molecule glycosylase studies with engineered 8-oxoguanine DNA damage sites show functional defects of a MUTYH polyposis variant.

Proper repair of oxidatively damaged DNA bases is essential to maintain genome stability. 8-Oxoguanine (7,8-dihydro-8-oxoguanine, 8-oxoG) is a dangerous DNA lesion because it can mispair with adenine (A) during replication resulting in guanine to thymine transversion mutations. MUTYH DNA glycosylase is responsible for recognizing and removing the adenine from 8-oxoG:adenine (8-oxoG:A) sites. Biallelic mutations in the MUTYH gene predispose individuals to MUTYH-associated polyposis (MAP), and the most commonly observed mutation in some MAP populations is Y165C. Tyr165 is a ‘wedge’ residue that intercalates into the DNA duplex in the lesion bound state. Here, we utilize single molecule fluorescence microscopy to visualize the real-time search behavior of Escherichia coli and Mus musculus MUTYH WT and wedge variant orthologs on DNA tightropes that contain 8-oxoG:A, 8-oxoG:cytosine, or apurinic product analog sites. We observe that MUTYH WT is able to efficiently find 8-oxoG:A damage and form highly stable bound complexes. In contrast, MUTYH Y150C shows decreased binding lifetimes on undamaged DNA and fails to form a stable lesion recognition complex at damage sites. These findings suggest that MUTYH does not rely upon the wedge residue for damage site recognition, but this residue stabilizes the lesion recognition complex.

Active Instrument Engagement Combined with a Real-Time Database Search for Improved Performance of Sample Multiplexing Workflows.

Quantitative proteomics employing isobaric reagents has been established as a powerful tool for biological discovery. Current workflows often utilize a dedicated quantitative spectrum to improve quantitative accuracy and precision. A consequence of this approach is a dramatic reduction in the spectral acquisition rate, which necessitates the use of additional instrument time to achieve comprehensive proteomic depth. This work assesses the performance and benefits of online and real-time spectral identification in quantitative multiplexed workflows. A Real-Time Search (RTS) algorithm was implemented to identify fragment spectra within milliseconds as they are acquired using a probabilistic score and to trigger quantitative spectra only upon confident peptide identification. The RTS-MS3 was benchmarked against standard workflows using a complex two-proteome model of interference and a targeted 10-plex comparison of kinase abundance profiles. Applying the RTS-MS3 method provided the comprehensive characterization of a 10-plex proteome in 50% less acquisition time. These data indicate that the RTS-MS3 approach provides dramatic performance improvements for quantitative multiplexed experiments.

Reinforcement learning-based real time search algorithm for routing optimisation in wireless sensor networks using fuzzy link cost estimation

Internet of things is a technological advancement of wireless sensor networks (WSNs) which are characterised by highly complex, large scale, heterogeneous, dynamically changing and asymmetric networks. Such constraints make routing in WSNs a difficult task. This paper introduces fuzzy link cost estimation-based real time search routing algorithm (fuzzy RTS) in which link cost estimation is obtained from physical and MAC layer parameters like residual energy, packet drop rate and RSSI. Its performance has been evaluated with traditional reinforcement learning-based algorithms like real time search, adaptive tree, ant routing and constrained flooding algorithms on the basis of metrics like throughput, loss rate, success rate, energy consumption, energy efficiency and node battery life. The simulation results reveal that fuzzy RTS algorithm is most appropriate reinforcement learning-based routing algorithm among given algorithms for ensuring energy efficient and QoS aware routing in dynamically changing, asymmetric and unreliable environment of WSNs.

Reinforcement learning-based real time search algorithm for routing optimisation in wireless sensor networks using fuzzy link cost estimation

Internet of things is a technological advancement of wireless sensor networks (WSNs) which are characterised by highly complex, large scale, heterogeneous, dynamically changing and asymmetric networks. Such constraints make routing in WSNs a difficult task. This paper introduces fuzzy link cost estimation-based real time search routing algorithm (fuzzy RTS) in which link cost estimation is obtained from physical and MAC layer parameters like residual energy, packet drop rate and RSSI. Its performance has been evaluated with traditional reinforcement learning-based algorithms like real time search, adaptive tree, ant routing and constrained flooding algorithms on the basis of metrics like throughput, loss rate, success rate, energy consumption, energy efficiency and node battery life. The simulation results reveal that fuzzy RTS algorithm is most appropriate reinforcement learning-based routing algorithm among given algorithms for ensuring energy efficient and QoS aware routing in dynamically changing, asymmetric and unreliable environment of WSNs.

The FRATS project: real-time searches for fast radio bursts and other fast transients with LOFAR at 135 MHz

Context. In the previous decade, two new classes of fast radio transients were detected: the Galactic, rotating radio transients (RRATs) and the extragalactic fast radio bursts (FRBs). If the detectable emission of these objects extends to lower radio frequencies, the LOw Frequency ARray (LOFAR) is ideally suited to seek and localize these transients at frequencies of 10–250 MHz. This is due to LOFAR’s sensitivity, diverse beamform capabilities, and transient buffers for the individual elements that allow post-event imaging of events, potentially at arcsecond resolution. Aims. Our aim is to identify and localize pulses at frequencies below 250 MHz and, in the case of nondetections, derive upper limits on the sky and volume rates of FRBs. Methods. A real-time search program for fast radio transients is installed on the LOFAR systems which runs commensally with other observations, and uses the wide incoherent LOFAR beam (11.25 deg2 at 150 MHz). Buffered data from hundreds of dipoles are used to reconstruct the direction and polarization information of the event, and to distinguish between celestial, terrestrial, and instrumental origins. Results. Observations were taken covering either the frequency range 119–151 MHz or in four frequency bands, each of 2 MHz in width, centered at 124, 149, 156, and 185 MHz. A first pilot survey covered a range of dispersion measures (DM) below 120 pc cm−3, focusing on Galactic sources, and resulted in an upper limit on the transient rate at LOFAR frequencies of less than 1500 events per sky per day above a fluency of 1.6 kJy ms for an 8-ms pulse. A second pilot survey covered a range of DMs below 500 pc cm−3, focusing on extragalactic sources to about 1 Gpc, and resulted in an upper limit of 1400 events per sky per day above a fluency of 6.0 kJy ms for an 8-ms pulse. Using a model for the distance-DM relationship, this equates to an upper limit of 134 events per Gpc3 per day.

An Intelligent Implementation of Fuzzy Search Based Nonlinear Droop Control Scheme for Multi-Terminal DC Transmission Systems

Multi-Terminal DC (MTDC) transmission systems typically operate under active power and DC voltage ( $P$ - $U_{DC}$ ) droop control for active power dispatch, and its design is greatly influenced by DC network characteristics. Hence, this paper firstly analyzes $P$ - $U_{DC}$ droop characteristics when implemented on Grid-Side Voltage-Source Converters (GSVSCs) of a MTDC network, in which variations of DC cables’ resistances with temperature are taken into account. The analysis establishes that the actual droop characteristics of GSVSCs are nonlinear, different from the widely assumption of linear relationships. This finding is the fundamental for the design of the accurate power dispatch for GSVSCs. Therefore, an improved Fuzzy Search based Droop Control (FSDC) scheme for MTDC systems is proposed, which performs real-time search to capture the accurate droop curves of GSVSCs over full range of permissible DC voltage and active power. The FSDC is implemented without the assumption of the prior knowledge of DC network parameters which are subject to variations and uncertainty, and instead the accurate droop curves are periodically obtained and updated. The technical viability of the FSDC and its superiority over the conventional linear droop scheme under the variations of DC cable resistances and power flow variations is verified by a four-terminal HVDC model established in MATLAB/Simulink.

Multi-Dimensional Joint Prediction Model for IoT Sensor Data Search

In recent years, with the rapid deployment of various Internet of Things (IoT) devices, it becomes a crucial and practical challenge to enable real-time search for objects, data, and services in the Internet of Everything. The IoT data prediction model can not only provide solutions for the real-time acquisition of the IoT sensor data but also provide more meaningful applications than the traditional IoT event detection model. In this paper, we use the complex time series formed by various types of sensors to establish a multi-dimensional feature selection model and a dynamic sensor-data prediction model. Compared with the traditional data prediction model, our model improves the accuracy and stability of the long-term prediction results of the IoT sensor data. Finally, we evaluate our prediction model using the Intel Berkeley Research Lab sensor data with an accuracy of over 98% and 92% accuracy on the Chicago Park District weather&water data.

Big Data Analysis: Medical Accident

Background/Objectives: Medical accidents have been happening in major large hospitals and real-time search queries for portal sites are increasing. We purpose to provide basic data on medical dispute resolution and medical accident prevention.Method/Statistical Analysis: We based data collected through educational and academic information sources(RISS) and *aver search as big data, and analyzed the keywords and the network analyzed by the ‘Medical accident’ keyword. The frequency, ranking, and connection of the keywords were analyzed through the process of processing the collected unstructured text into the data suitable for research and analysis.Findings: As a result, in case of the academic research related to the medical accidents, the study on the medical dispute is most active. In the analysis of the network, the medical accidents lead to the medical disputes and are mostly resolved by law in a manner that judge criminal responsibility and other responsibilities(punishment and compensation).Analysis of search words from news, blogs and Internet cafes, and the most frequently mentioned keyword related to medical accidents was criminal responsibility. In the analysis of the network it will be as follows: the handling of medical accidents involving criminal liability and other liabilities shall result in punitive damages resulting from legal proceedings or monetary damages resulting from civil agreements.Improvements/Applications: We suggest that the human, institutional, and physical environment should be improved to avoid medical accidents, conflicts and litigation.

LION LBD: a literature-based discovery system for cancer biology.

MotivationThe overwhelming size and rapid growth of the biomedical literature make it impossible for scientists to read all studies related to their work, potentially leading to missed connections and wasted time and resources. Literature-based discovery (LBD) aims to alleviate these issues by identifying implicit links between disjoint parts of the literature. While LBD has been studied in depth since its introduction three decades ago, there has been limited work making use of recent advances in biomedical text processing methods in LBD.ResultsWe present LION LBD, a literature-based discovery system that enables researchers to navigate published information and supports hypothesis generation and testing. The system is built with a particular focus on the molecular biology of cancer using state-of-the-art machine learning and natural language processing methods, including named entity recognition and grounding to domain ontologies covering a wide range of entity types and a novel approach to detecting references to the hallmarks of cancer in text. LION LBD implements a broad selection of co-occurrence based metrics for analyzing the strength of entity associations, and its design allows real-time search to discover indirect associations between entities in a database of tens of millions of publications while preserving the ability of users to explore each mention in its original context in the literature. Evaluations of the system demonstrate its ability to identify undiscovered links and rank relevant concepts highly among potential connections.Availability and implementationThe LION LBD system is available via a web-based user interface and a programmable API, and all components of the system are made available under open licenses from the project home page http://lbd.lionproject.net.Supplementary information Supplementary data are available at Bioinformatics online.

The Apertif Monitor for Bursts Encountered in Real-time (AMBER) auto-tuning optimization with genetic algorithms

Real-time searches for faint radio pulses from unknown radio transients are computationally challenging. Detections become further complicated due to continuously increasing technical capabilities of transient surveys: telescope sensitivity, searched area of the sky, number of antennas or dishes, temporal and frequency resolution. The new Apertif transient survey on the Westerbork telescope happens in real-time on GPUs by means of the single-pulse search pipeline AMBER (Sclocco, 2017). AMBER initially carries out auto tuning: it finds the most optimal configuration of user-controlled parameters per each of four pipeline kernels so that each kernel performs its task as fast as possible. The pipeline uses a brute-force (BF) exhaustive search which in total takes 5 - 24 hours to run depending on the processing cluster architecture. We apply more heuristic, biologically driven genetic algorithms (GAs) to limit the exploration of the total parameter space, tune all four kernels together and reduce the tuning time to few hours. Our results show that after only few hours of tuning, GAs always find similar or even better configurations for all kernels together than the combination of single kernel configurations tuned by the BF approach. At the same time, by means of their genetic operators, GAs converge into better solutions than those obtained by pure random searches. The explored multi-dimensional parameter space is very complex and has multiple local optima as the evolution of randomly generated configurations does not always guarantee global solution.

Effects of Preferential Counterion Interactions on the Specificity of RNA Folding.

The real-time search for native RNA structure is essential for the operation of regulatory RNAs. We previously reported that a fraction of the Azoarcus ribozyme achieves a compact structure in less than a millisecond. To scrutinize the forces that drive initial folding steps, we used time-resolved SAXS to compare the folding dynamics of this ribozyme in thermodynamically isostable concentrations of different counterions. The results show that the size of the fast-folding population increases with the number of available counterions and correlates with the flexibility of initial RNA structures. Within 1 ms of folding, Mg2+ exhibits a smaller preferential interaction coefficient per charge, ΔΓ+/ Z, than Na+ or [Co(NH3)6]3+. The lower ΔΓ+/ Z corresponds to a smaller yield of folded RNA, although Mg2+ stabilizes native RNA more efficiently than other ions at equilibrium. These results suggest that strong Mg2+-RNA interactions impede the search for globally native structure during early folding stages.

Memory-Augmented Monte Carlo Tree Search

This paper proposes and evaluates Memory-Augmented Monte Carlo Tree Search (M-MCTS), which provides a new approach to exploit generalization in online real-time search. The key idea of M-MCTS is to incorporate MCTS with a memory structure, where each entry contains information of a particular state. This memory is used to generate an approximate value estimation by combining the estimations of similar states. We show that the memory based value approximation is better than the vanilla Monte Carlo estimation with high probability under mild conditions. We evaluate M-MCTS in the game of Go. Experimental results show that M-MCTS outperforms the original MCTS with the same number of simulations.

Integration of Visualization Techniques to Algorithms of Optimization of the Metaheuristics Ant Colony

The search guided by a user contributes to solving optimization problems. No adequate mechanisms for algorithms that use the metaheuristic Ant Colony (ACO) to achieve this interaction are known. This paper proposes a model of integration of visualization techniques in these algorithms that allows the user to interact with real-time search and guide her. A software tool was implemented to solve Traveling Salesman Problem (TSP) with ACO algorithm according to the proposed model. An experimental analysis with the developed tool was performed and the results showed the eficiency of the model, inding better solutions to problems TSP in less time.

Deep neural networks to enable real-time multimessenger astrophysics

Gravitational wave astronomy has set in motion a scientific revolution. To further enhance the science reach of this emergent field, there is a pressing need to increase the depth and speed of the gravitational wave algorithms that have enabled these groundbreaking discoveries. To contribute to this effort, we introduce Deep Filtering, a new highly scalable method for end-to-end time-series signal processing, based on a system of two deep convolutional neural networks, which we designed for classification and regression to rapidly detect and estimate parameters of signals in highly noisy time-series data streams. We demonstrate a novel training scheme with gradually increasing noise levels, and a transfer learning procedure between the two networks. We showcase the application of this method for the detection and parameter estimation of gravitational waves from binary black hole mergers. Our results indicate that Deep Filtering significantly outperforms conventional machine learning techniques, achieves similar performance compared to matched-filtering while being several orders of magnitude faster thus allowing real-time processing of raw big data with minimal resources. More importantly, Deep Filtering extends the range of gravitational wave signals that can be detected with ground-based gravitational wave detectors. This framework leverages recent advances in artificial intelligence algorithms and emerging hardware architectures, such as deep-learning-optimized GPUs, to facilitate real-time searches of gravitational wave sources and their electromagnetic and astro-particle counterparts.

Review On: Detection of Spam Comments Using NLP Algorithm

Detecting trending topics is perfect to summarize information getting from social media. To extract what topic is becoming hot on online media is one of the challenges. As we considering social media so social services are opportunity for spamming which greatly affect on value of real time search. Therefore the next task is to control spamming from social networking sites. For completing these challenges different concepts of data mining will be used. For now whatever work has been done is narrated below like spam control using natural language processing for preprocessing and clustering. One account has been created for making it real.

Sensor Information Retrieval From Internet of Things: Representation and Indexing

Billions of devices are connected in the Internet of Things (IoT)-based sensor networks and they continuously generate a large volume of data. In order to get access to specific data, which is crucial to enable a myriad of new intelligent applications, efficient information retrieval becomes an imminent need for IoT. However, sensor information in the physical world can be heterogeneous, high dimensional, and voluminous due to the complex and dynamic environments. In this paper, we first investigate several IoT search scenarios and propose a uniform representation model for sensor information recordings. Four query models are designed to represent all possible information query styles. With these models, we develop information retrieval architecture for IoT. In essence, an indexing mechanism called efficiency maximization and cost minimization is proposed to solve the property selection problem in the process of index construction and update. Meanwhile, a novel real-time grid R-tree structure is designed to support historical and real-time search for spatiotemporal observation data. Simulation results based on real-world IoT data sets show that storage space is considerably reduced with the sensor model. Furthermore, the proposed indexing mechanisms can improve retrieval efficiency and accuracy, and ensure scalability for large-sized data simultaneously.