Query Resolution Research Articles

A Review of Mobile Applications Available in the App and Google Play Stores Used During the COVID-19 Outbreak.

PurposeThe objective of this paper was to review the functionalities and effectiveness of the free mobile health applications available in the Google Play and App stores used in Saudi Arabia, Italy, Singapore, the United Kingdom, USA, and India during the COVID-19 outbreak.MethodsThis study adopted a systematic search strategy to identify the free mobile applications available in the App and Google Play stores related to the COVID-19 outbreak. According to the PRISMA flowchart of the search, only 12 applications met the inclusion criterion.ResultsThe 12 mobile applications that met the inclusion criterion were: Mawid, Tabaud, Tawakkalna, Sehha, Aarogya setu, TraceTogether, COVID safe, Immuni, COVID symptom study, COVID watch, NHS COVID-19, and PathCheck. The following features and functionalities of the apps were described: app overview (price, ratings, android, iOS, developer/owner, country, status), health tools (user status-risk assessment, self-assessment, E-pass integration, test results reporting, online consultation, contact tracing), learning options (personalized notes, educational resources, COVID-19 information), communication tools (query resolution, appointments, social network, notifications), app design (data visualization, program plan), networking tools (location mapping – GPS, connectivity with other devices), and safety and security options (alerts, data protection). Also, the effectiveness of the apps was analyzed.ConclusionThe analysis revealed that various applications have been developed for different functions like contact tracing, awareness building, appointment booking, online consultation, etc. However, only a few applications have integrated various functions and features such as self-assessment, consultation, support and access to information. Also, most of the apps are focused on contact tracing, while very few are dedicated to raising awareness and sharing information about the COVID-19 pandemic. Likewise, the majority of applications rely on GPS and Bluetooth technologies for relevant functions. No apps were identified that had built-in social media features. It is suggested to design and develop an integrated mobile health application with most of the features and functionalities analyzed in this study.

Financial Knowledge Graph Based Financial Report Query System

Annual Financial Reports are the core in the Banking Sector to publish its financial statistics. Extracting useful information from these complex and lengthy reports involves manual process to resolve the financial queries, resulting in delays and ambiguity in investment decisions. One of the major reasons is the lack of any standardization in the format and vocabulary used in the reports. An automated system for resolution of intelligent financial queries is therefore difficult to design. Several works have been proposed to overcome these problems using Information Extraction; however, they do not address the semantic interoperability of the reports across different institutions. This work proposed an automated querying engine to answer the financial queries using Ontology based Information Extraction. For Semantic modeling of financial reports, a Financial Knowledge Graph, assisted by Financial Ontology, has been proposed. The nodes are populated with entities, while links are populated with relationships using Information Extraction applied on annual reports. Two benefits have been provided by this system to stakeholders through automation: decision making through queries and generation of custom financial stories. The work can further be extended to other domains including healthcare and academia where physical reports are used for communication.

Completion of clinical trials in light of COVID-19

Completion of clinical trials in light of COVID-19

A hybrid flipped‐classroom approach for online teaching of biochemistry in developing countries during Covid‐19 crisis

Covid-19 pandemic, with millions of cases, has caused a huge socio-economic impact and impaired routine classroom teaching across the globe.1 While the situation is still not out of danger, online solutions for teaching are rapidly emerging and being constantly improved by educators, institutions and educational associations like ASBMB.2 As educators for over a decade, we have observed a reluctant behavior of students toward the online learning platforms in developing economies particularly due to poor internet connectivity, inferior technical know-how of the online tools, and hindered two-way communication between student and teacher. A recent surge in the user-friendly platforms such as Zoom, Google Meet, Cisco Webex, Microsoft Team and other options has shattered this reluctance and rapid turnout of students has been observed. With over a class of 200 students, we have been developing newer strategies and experimented with various formats of online platforms. Among the common modes that we used were, mode 1: conventional audio-delivery with PowerPoint slides on live platforms, mode 2: video and audio with animated graphics on live platforms, mode 3: recorded videos with audio and video support, and mode 4: Audio-visual one-to-one discussion and problem solving with conceptual understating. Provided the relatively inferior internet connectivity in remote parts of the developing countries, audio alone with animated PowerPoint slides (mode 1) gathered a better response with no absolute requirement of instructor video. However, the delivery was monotonous for most of the students who had access to better internet connectivity. In the second mode where both video and audio were delivered, the student connectivity with the instructor was better and the session was interactive, especially while answering questions of students where instructor's gestures had an impact. In mode 3, which was a prior recorded video, the learners appreciated the least disturbance and flexibility of learning but reacted negatively on the grounds of instructor-learner interaction. After observing the comments and feedback of learners, we practiced a hybrid approach that was a blend of above four modes, along with a popular and rapidly emerging flipped classroom strategy,3 where students were provided with slides, questionnaire and supporting open-source links prior to the delivery of video lecture at least 24 h in advance. The student's queries were also made available to the tutor before the lecture stated. On the start of a routine online class, we started with format 1, and on completion of the concept, we switched to mode 3, with a final one-way resolution of previous day queries, followed by a two-way communication for clearing doubts for the current session. Our approach was tested for a topic carbohydrate metabolism with a 40-minute trial class on three different platforms, Zoom,4 WizIQ5 and Google Meet.6 Although the platform did not have a much impact on the learning outcome as most of the impact was based on content and delivery, ease of access and preference to the specific tool could not be justified. Furthermore, internet connectivity is an issue with some regions of the developing economies which was observed as a major hurdle in the mode 1 and mode 4, pre-recorded videos with enough buffer time received a good feedback from the users. Additionally, during our proposed hybrid approach for users with poor internet last phase of interactive session was provided in recorded from for later use and text based resolution of queries was performed. In summary, we would comment that the stated approach could be highly beneficial and could be extrapolated to subjects beyond biochemistry, that shall benefit a larger learner-instructor community in the times of pandemic crisis. Authors dec-lare no conflict of interest and do not endorse any online software or tool in this article and did not compare any merits of online teaching tools.

Scalable Decentralized Indexing and Querying of Multi-Streams in the Fog

NOA-AID (Network Overlays for Adaptive information Aggregation, Indexing and Discovery on the fog) is an approach for decentralized indexing, aggregation and discovery of data belonging to streams. It is organized on two network layers. The upper layer is in charge of delivering an information discovery approach by providing a distributed index structure. The lower layer is devoted to resource aggregation based on epidemic protocols designed for highly dynamic environment, well suited to stream-oriented scenarios. It defines a flexible approach to express queries targeting highly heterogeneous data, as well as a self-organizing dynamic system allowing the optimal resolution of queries on the most suitable stream producers. The paper also presents a theoretical study and discusses the costs related to information management operations; it also gives an empirical validation of findings. Finally, it reports an extended experimental evaluation that demonstrated the ability of NOA-AID to be effective and efficient for retrieving information extracted from streams in highly-dynamic and distributed processing architectures.

Compressed Indexes for Fast Search of Semantic Data

The sheer increase in volume of RDF data demands efficient solutions for the triple indexing problem, that is to devise a compressed data structure to compactly represent RDF triples by guaranteeing, at the same time, fast pattern matching operations. This problem lies at the heart of delivering good practical performance for the resolution of complex SPARQL queries on large RDF datasets. In this work, we propose a trie-based index layout to solve the problem and introduce two novel techniques to reduce its space of representation for improved effectiveness. The extensive experimental analysis, conducted over a wide range of publicly available real-world datasets, reveals that our best space/time trade-off configuration substantially outperforms existing solutions at the state-of-the-art, by taking 30-60 percent less space and speeding up query execution by a factor of 2 - 81×.

Spatio-Fog: A green and timeliness-oriented fog computing model for geospatial query resolution

Geospatial data analysis is an emerging area of research today. Systems need to respond to user requests in a timely manner. In this paper we have proposed a fog computing framework namely Spatio-Fog, where the fog devices contain the geospatial data of their current region and process geospatial queries using resources in the proximity. The geospatial query resolution is performed by the fog device either itself or using cloud servers or fog device of other region depending on the geographical region related to the geospatial query. We have performed both empirical study and experimental analysis to demonstrate feasibility of our proposed approach. The empirical study illustrates that the proposed architecture Spatio-Fog reduces the power consumption and delay by approximately 43–47% and 47–83% respectively over the use of existing geospatial query resolution system. The experimental analysis demonstrates that the proposed framework reduces the power consumption and delay by 30–60% approximately than the existing geospatial query resolution system.

Long-term Apomorphine Infusion Users Versus Short-term Users: An International Dual-center Analysis of the Reasons for Discontinuing Therapy

A retrospective analysis at 2 specialist centers was undertaken to determine the long-term efficacy of subcutaneous apomorphine infusion (APO), rates and reasons for discontinuation, and factors that might contribute to discontinuation. Demographics, clinical outcomes data, and reasons for discontinuation were collected for patients treated with APO at Chulalongkorn Centre of Excellence for Parkinson's Disease and Related Disorders, Bangkok, Thailand (n = 36) and Fundacion Jimemez Diaz Universidad Autonoma de Madrid, Spain (n = 16). There were 19 (52.7%) patients in the Thai cohort and 10 (62.5%) patients in the Spanish cohort who discontinued treatment within around 6 months of initiation, most commonly due to skin nodules (Thai cohort) and perceived lack of efficacy (Spanish cohort). Those who continued APO tended to stay on treatment. In both cohorts, APO resulted in significant reductions in Unified Parkinson's Disease Rating Scale 3 motor scores, daily OFF time, and levodopa-equivalent dose in patients who subsequently stopped therapy, suggesting APO is clinically effective even when "lack of efficacy" is stated as a reason for discontinuing. Daily OFF hours after APO therapy was found to be a significant predictive factor for APO discontinuation with an odds ratio of 5.952 (P = 0.040). The cutoff point that determined APO discontinuation was calculated to be 1.75 or more OFF hours (sensitivity, 84.6%; specificity, 63.2%). Apomorphine infusion is a minimally invasive therapy and therefore very easy to discontinue if difficulties arise. This fact might explain the high dropout rate of this technique. Successful long-term adherence to APO therapy requires a multidisciplinary health care team approach including regular patient follow-up and assessment and prompt resolution of queries and concerns.

Centralized Clinical Trial Imaging Data Management: Practical Guidance from a Comprehensive Cancer Center's Experience.

Medical imaging is an integral part of clinical trial research and it must be managed properly to provide accurate data to the sponsor in a timely manner (Clune in Cancer Inform 4:33-56, 2007; Wang et al. in Proc SPIE Int Soc Opt Eng 7967, 2011). Standardized workflows for site qualification, protocol preparation, data storage, retrieval, de-identification, submission, and query resolution are paramount to achieve quality clinical trial data management such as reducing the number of imaging protocol deviations and avoiding delays in data transfer. Centralization of data management and implementation of relational databases and electronic workflows can help maintain consistency and accuracy of imaging data. This technical note aims at sharing the practical implementation of our centralized clinical trial imaging data management processes to avoid the fragmentation of tasks among various disease centers and research staff, and enable us to provide quality, accurate, and timely imaging data to clinical trial sponsors.

STUDENT INFORMATION AI CHATBOT

-Automatic conversation system is an intelligent human machine interaction using natural language. Main goal of it is to allow the user and machine to make a natural harmonious conversation. Thus enabling the machine to recognize human motivation and to respond accurately, is not only an important manifestation of advanced intelligence, but also a very challenging work in harmonious human interaction field [1]. A conversation system consists of speech recognition, speech synthesis, and dialogue management and conversation generation. In this research, we focus on automatic generation of conversation between a computer and a human being with little knowledge of the computer.In this paper, we influenced a PC to end up a preparation to accomplice of a man who isn't great at discussion, to wind up a band together with a man. Therefore, in this research, we are focusing specifically on “chat by developing an interactive AI which converse mainly by using machine learning. We perform a word unit prediction by using “Hill Climbing” algorithm and based on relevancy ranking, the relevant conversation is made.Our main focus, is to build a Student chat bot which helps the colleges to have 24*7 automated query resolution. This helps the students to have the right information from the trusted source. Also the administrationof information is made easy for the institutions.

A Novel Approach for Information Discovery in Wireless Sensor Grids

Multi-dimensional Wireless sensor grids (WSG)s are deployed in complex environments to sense and collect data relating to multiple attributes (multi-dimensional data). Such networks present unique challenges to data dissemination, data storage of in-network information discovery. However, in order to fully exploit these networks for mission-critical applications, energy-efficient and scalable solutions for information discovery are essential. In this paper, we propose a novel and adaptive method for information discovery for multi-dimensional WSGs that can significantly increase network lifetime and minimize query processing latency, resulting in quality of service improvements that are of immense benefit to mission-critical applications. Further, we investigate efficient strategies for information discovery in large-scale wireless sensor networks and propose the Adaptive Multi-Dimensional Multi-Resolution Architecture (A-MDMRA) that efficiently combines “push” and “pull” strategies for information discovery. The A-MDMRA also adapts to variations in the frequencies of events and queries in the network to construct optimal routing structures. We present simulation results to show that the proposed approach to information discovery offers significant improvements on query resolution latency compared with current approaches. We observe that our proposed methods outperform existing schemes such as double rulings, comb needle and Time-Parameterized Data Centric Storage by up to 14% in terms of query resolution latency and up to 20% in terms of energy-efficiency.

Searching the Web of Things

Technological advances allow more physical objects to connect to the Internet and provide their services on the Web as resources. Search engines are the key to fully utilize this emerging Web of Things, as they bridge users and applications with resources needed for their operation. Developing these systems is a challenging and diverse endeavor due to the diversity of Web of Things resources that they work with. Each combination of resources in query resolution process requires a different type of search engine with its own technical challenges and usage scenarios. This diversity complicates both the development of new systems and assessment of the state of the art. In this article, we present a systematic survey on Web of Things Search Engines (WoTSE), focusing on the diversity in forms of these systems. We collect and analyze over 200 related academic works to build a flexible conceptual model for WoTSE. We develop an analytical framework on this model to review the development of the field and its current status, reflected by 30 representative works in the area. We conclude our survey with a discussion on open issues to bridge the gap between the existing progress and an ideal WoTSE.

Word Ordering and Document Adjacency for Large Loop Closure Detection in 2-D Laser Maps

We address in this letter the problem of loop closure detection for laser-based simultaneous localization and mapping (SLAM) of very large areas. Consistent with the state of the art, the map is encoded as a graph of poses, and to cope with very large mapping capabilities, loop closures are asserted by comparing the features extracted from a query laser scan against a previously acquired corpus of scan features using a bag-of-words (BoW) scheme. Two contributions are here presented. First, to benefit from the graph topology, feature frequency scores in the BoW are computed not only for each individual scan but also from neighboring scans in the SLAM graph. This has the effect of enforcing neighbor relational information during document matching. Second, a weak geometric check that takes into account feature ordering and occlusions is introduced that substantially improves loop closure detection performance. The two contributions are evaluated both separately and jointly on four common SLAM datasets and are shown to improve the state-of-the-art performance both in terms of precision and recall in most of the cases. Moreover, our current implementation is designed to work at nearly frame rate, allowing loop closure query resolution at nearly 22 Hz for the best case scenario and 2 Hz for the worst case scenario.

Performance improvements for search systems using an integrated cache of lists + intersections

Modern information retrieval systems use several levels of caching to speedup computation by exploiting frequent, recent or costly data used in the past. Previous studies show that the use of caching techniques is crucial in search engines, as it helps reducing query response times and processing workloads on search servers. In this work we propose and evaluate a static cache that acts simultaneously as list and intersection cache, offering a more efficient way of handling cache space. We also use a query resolution strategy that takes advantage of the existence of this cache to reorder the query execution sequence. In addition, we propose effective strategies to select the term pairs that should populate the cache. We also represent the data in cache in both raw and compressed forms and evaluate the differences between them using different configurations of cache sizes. The results show that the proposed Integrated Cache outperforms the standard posting lists cache in most of the cases, taking advantage not only of the intersection cache but also the query resolution strategy.

PRM208 - Important Methodological Considerations and Strategies for Resolution for Multinational Retrospective Chart Review Studies

In the absence of secondary sources of health care data, chart review studies are necessary to fulfill peri- and post-approval evidence gaps such as informing on patient profiles, treatment effectiveness, and clinical and safety outcomes. Multinational studies (conducted in more than one country) are essential in understanding how to best address global medical needs; however the conduct of these studies poses its own set of challenges. Our objective was to explore the design and operational challenges and strategies to overcome these challenges in order to ensure successful conduct of multinational chart review studies. Fourteen recent multinational chart review case studies were evaluated to describe lessons learned in terms of key methodological considerations and strategies for resolution. Eight studies (57%) were comprised of 3 or more countries and 7 studies (50%) were comprised of European countries only. Across studies, common scientific and operational considerations included identifying country-specific ethics and privacy regulations (including patient informed consent) and accounting for variance in treatment patterns and safety reporting. Recommendations include pilot-testing the case report form (CRF) in participating countries to ensure data points of interest are available, sufficient staff training on study purpose, outcomes and electronic CRF navigation, and creation of detailed training materials and guidelines for data abstraction. Additionally, in order to ensure uniformity of data collection across sites, the use of a standardized data collection tool and the implementation of quality control guidelines for data cleaning and query resolution is recommended. Multinational chart reviews are an effective methodology for capturing tailored, patient-level data and offer an important opportunity to inform on global clinical data. While the conduct of multinational chart review studies can be challenging due to inter-country variance, careful consideration of key design and operational challenges can allow for successful study implementation.

A Stable Approach for Routing Queries in Unstructured P2P Networks

Finding a document or resource in an unstructured peer-to-peer network can be an exceedingly difficult problem. In this paper we propose a query routing approach that accounts for arbitrary overlay topologies, nodes with heterogeneous processing capacity, e.g., reflecting their degree of altruism, and heterogenous class-based likelihoods of query resolution at nodes which may reflect query loads and the manner in which files/resources are distributed across the network. The approach is shown to be stabilize the query load subject to a grade of service constraint, i.e., a guarantee that queries' routes meet pre-specified class-based bounds on their associated a priori probability of query resolution. An explicit characterization of the capacity region for such systems is given and numerically compared to that associated with random walk based searches. Simulation results further show the performance benefits, in terms of mean delay, of the proposed approach. Additional aspects associated with reducing complexity, estimating parameters, and adaptation to class-based query resolution probabilities and traffic loads are studied.

An efficient ant colony optimization strategy for the resolution of multi-class queries

Ant Colony Optimization is a bio-inspired computational technique for establishing optimal paths in graphs. It has been successfully adapted to solve many classical computational problems, with considerable results. Nevertheless, the attempts to apply ACO to the question of multidimensional problems and multi-class resource querying have been somewhat limited. They suffer from either severely decreased efficiency or low scalability, and are usually static, custom-made solutions with only one particular use. In this paper we employ Angry Ant Framework, a multipheromone variant of Ant Colony System that surpasses its predecessor in terms of convergence quality, to the question of multi-class resource queries. To the best of the authors knowledge it is the only algorithm capable of dynamically creating and pruning pheromone levels, which we refer to as dynamic pheromone stratification. In a series of experiments we verify that, due to this pheromone level flexibility, Angry Ant Framework, as well as our improvement of it called Entropic Angry Ant Framework, have significantly more potential for handling multi-class resource queries than their single pheromone counterpart. Most notably, the tight coupling between pheromone and resource classes enables convergence that is both better in quality and more stable, while maintaining a sublinear cost.

Autonomous Cloud Federation for High-Throughput Queries over Voluminous Datasets

The breadth and depth of information being generated and stored continues to grow rapidly, causing an information explosion. Observational devices and remote sensing equipment are no exception here, giving researchers new avenues for detecting and predicting phenomena at a global scale. To cope with increasing storage loads, hybrid clouds offer an elastic solution that also satisfies processing and budgetary needs. In this article, the authors describe their algorithms and system design for dealing with voluminous datasets in a hybrid cloud setting. Their distributed storage framework autonomously tunes in-memory data structures and query parameters to ensure efficient retrievals and minimize resource consumption. To circumvent processing hotspots, they predict changes in incoming traffic and federate their query resolution structures to the public cloud for processing. They demonstrate their framework's efficacy on a real-world, petabyte dataset consisting of more than 20 billion files.

The latest research advance in media involved in sepsis

Sepsis and septic shock are the major causes of death in intensive care units (ICUs) worldwide. A large amount of studies on their pathophysiology have revealed an imbalance in the inflammatory network leading to tissue damage, organ failure, and ultimately, death. Cytokines, proteases, lipid mediators, vasoactive peptides, and cell stress markers play key roles in pathophysiology of sepsis. Although anti-inflammatory mediators can neutralize the promoting role of pro-inflammatory mediators, but persistent immune regulation may cause host susceptibility to concurrent infections. Therefore, it is a great challenge to seek effective clinical therapy against sepsis. To understand the complicated interplay between pro- and anti-inflammatory agents in sepsis, and their interaction in signal transduction and immune regulation in sepsis constitute vital significance to the prevention and control of sepsis. Summarize the latest findings about mediators associated with sepsis and innate and adaptive immune system at home and abroad in recent years, and illustrate the impact of its effects on sepsis, which may lead to resolution of many unexplored queries in the future.

Dragon: Multidimensional range queries on distributed aggregation trees

Distributed query processing is of paramount importance in next-generation distribution services, such as Internet of Things (IoT) and cyber–physical systems. Even if several multi-attribute range queries supports have been proposed for peer-to-peer systems, these solutions must be rethought to fully meet the requirements of new computational paradigms for IoT, like fog computing. This paper proposes dragon, an efficient support for distributed multi-dimensional range query processing targeting efficient query resolution on highly dynamic data. In dragon nodes at the edges of the network collect and publish multi-dimensional data. The nodes collectively manage an aggregation tree storing data digests which are then exploited, when resolving queries, to prune the sub-trees containing few or no relevant matches. Multi-attribute queries are managed by linearizing the attribute space through space filling curves. We extensively analysed different aggregation and query resolution strategies in a wide spectrum of experimental set-ups. We show that dragon manages efficiently fast changing data values. Further, we show that dragon resolves queries by contacting a lower number of nodes when compared to a similar approach in the state of the art.