Number Of Data Sources Research Articles

Modeling the effect of LULC change on water quantity and quality in Big Creek Lake Watershed, South Alabama USA

The land use and land cover (LULC) of a watershed play an important role in controlling its hydrological processes. With the help of applying the Soil and Water Assessment Tool (SWAT), this study aims to quantify the impact of changing LULC on hydrological responses and water quality in the Big Creek Lake watershed in Mobile County, South Alabama. A number of data sources were input into the SWAT model as part of its calibration and validation, including land use and land cover (LULC), weather variables, digital elevation models (DEMs), soil properties, and measured streamflows. The total monthly streamflow increased by about 62 m3/s and the average nitrogen and phosphorus are estimated to have increased by about 3,172 kg/Ha and 892 kg/Ha per year respectively over the thirty years because of the increasing agricultural land (11,406 acres), urban development (3,350 acres), and decreasing forested areas (11,482 acres). This research could be helpful for water resource managers and planners by incorporating the results in the monitoring and planning for the future.

Data Sources and Models for Integrated Mobility and Transport Solutions.

The number of data sources and models in the mobility and transport domain strongly proliferated in the last decade. Most formats have been created to enable specific and innovative applications. On the other hand, the available data models present a certain degree of complexity in terms of their integration and management due to partial overlaps, and in most cases, they could be exploited alternatively to implement the same smart and latest innovative solutions. This paper offers an overview of data models, standards and their relationships. A second contribution highlights any possible exploitation of data models for implementing operational processes for city transportation management and for the feeding of simulation and optimization processes that produce other data results in other data models. The final goal in most cases is the monitoring and control of city transport conditions, as well as the tactic and strategic planning of city infrastructure. This work was developed in the context of the CN MOST, a national center of sustainable mobility in Italy, and it is based on exploiting the Snap4City platform.

Fair Rewards in Federated Learning: A Novel Approach with Adjusted OR-TMC Shapley Value Approximation Algorithm

Federated Learning (FL), a new private and secure Machine Learning (ML) approach, faces a big difficulty when it comes to sharing profits with data producers. Shapley Values (SV) have been proposed as a fair incentive system to remedy this, but it is challenging to determine the SV with accuracy. Therefore, SV calculation is problematic since the number of necessary federated models rises exponentially with the number of data sources. As a result, an effective approximation approach is required. The One Round Model Reconstruction (OR) and Truncated Monte Carlo Shapley (TMC) approaches for SV approximation in FL are being improved and combined in this study. The proposed approach, Adjusted OR-TMC, combines TMC principles with OR and achieves a comparable level of accuracy over a shorter period. Because of this, Adjusted OR-TMC is the perfect OR replacement. The performance outcomes and underlying causes are covered in the study.

Emergency department-based injury surveillance information system: a conceptual model

BackgroundInjury data play a pivotal role in monitoring public health issues and Injury Surveillance Information Systems (ISIS) are useful for continuous data collection and analysis purposes. Since emergency department (ED) is usually the first place of referral for the injured people, the aim of this study was to develop a conceptual model for an ED-based ISIS.MethodsThis study was completed in 2020 and the Delphi technique (three rounds) was used to determine the main components of an ED-based ISIS. The participants were selected using the purposive sampling method. A 5-point Likert scale questionnaire was used for data collection and data were analyzed using descriptive statistics.ResultsIn the first, second, and third rounds of the Delphi study, 60, 44, and 28 experts participated, respectively. In the first and second rounds, most of the items including the personal data, clinical data, data sources, and system functions were found important. In the third round of the Delphi study, 13 items which did not reach a consensus in the previous rounds were questioned again and five items were removed from the final model.ConclusionAccording to the findings, various data elements and functions could be considered for designing an ED-based ISIS and a number of data sources should be taken into count to be integrated with this system. Although the conceptual model presented in the present study can facilitate designing the actual system, the final system needs to be implemented and used in practice to determine how it can meet users’ requirements.

Data Integration Framework to Collect Data from OT/IT Systems

Industry 4.0 and industrial data processing, due to its inherent possibilities, is gaining more and more emphasis in production companies these days. In a corporate environment, the age of equipment is extremely heterogeneous, in addition to state-of-the-art equipment, legacy systems can also be found in the machine park, which do not have appropriate communication protocols. Also, with the increase in the number of data sources, the management of data is becoming more and more challenging. Not only the operational technology, but also the connection of different IT systems and the extraction of data pose challenges. The different data processing use-cases using partly or entirely the same data sources, so it is necessary to extract and transmit the data to the target systems in a standard way, and avoiding an increase in the number of point-to-point interfaces. In this work we present a possible framework, to solve the above mentioned problems in industrial environment, with the introduction of standardized naming conventions, OT/IT gateways, data integration and distribution layers.

A robust fusion-extraction procedure with summary statistics in the presence of biased sources

Summary Information from multiple data sources is increasingly available. However, some data sources may produce biased estimates due to biased sampling, data corruption or model misspecification. Thus there is a need for robust data combination methods that can be used with biased sources. In this paper, a robust data fusion-extraction method is proposed. Unlike existing methods, the proposed method can be applied in the important case where researchers have no knowledge of which data sources are unbiased. The proposed estimator is easy to compute and employs only summary statistics; hence it can be applied in many different fields, such as meta-analysis, Mendelian randomization and distributed systems. The proposed estimator is consistent, even if many data sources are biased, and is asymptotically equivalent to the oracle estimator that uses only unbiased data. Asymptotic normality of the proposed estimator is also established. In contrast to existing meta-analysis methods, the theoretical properties are guaranteed for our estimator, even if the number of data sources and the dimension of the parameter diverge as the sample size increases. Furthermore, the proposed method provides consistent selection for unbiased data sources with probability approaching 1. Simulation studies demonstrate the efficiency and robustness of the proposed method empirically. The method is applied to a meta-analysis dataset to evaluate surgical treatment for moderate periodontal disease and to a Mendelian randomization dataset to study the risk factors for head and neck cancer.

Scaling up knowledge graph creation to large and heterogeneous data sources

RDF knowledge graphs (KG) are powerful data structures to represent factual statements created from heterogeneous data sources. KG creation is laborious and demands data management techniques to be executed efficiently. This paper tackles the problem of the automatic generation of KG creation processes declaratively specified; it proposes techniques for planning and transforming heterogeneous data into RDF triples following mapping assertions specified in the RDF Mapping Language (RML). Given a set of mapping assertions, the planner provides an optimized execution plan by partitioning and scheduling the execution of the assertions. First, the planner assesses an optimized number of partitions considering the number of data sources, type of mapping assertions, and the associations between different assertions. After providing a list of partitions and assertions that belong to each partition, the planner determines their execution order. A greedy algorithm is implemented to generate the partitions’ bushy tree execution plan. Bushy tree plans are translated into operating system commands that guide the execution of the partitions of the mapping assertions in the order indicated by the bushy tree. The proposed optimization approach is evaluated over state-of-the-art RML-compliant engines, and existing benchmarks of data sources and RML triples maps. Our experimental results suggest that the performance of the studied engines can be considerably improved, particularly in a complex setting with numerous triples maps and large data sources. As a result, engines that time out in complex cases are enabled to produce at least a portion of the KG applying the planner.

Construction of a Prediction Model for Distance Education Quality Assessment Based on Convolutional Neural Network.

This paper introduces the principles and operation steps of convolution and pooling of convolutional neural networks in detail. In view of the shortcomings of fixed sampling points and single receptive field in traditional convolution and pooling forms, deformable convolution and deformable pooling are introduced to enhance the network's ability to adapt to image details and large displacement problems. The concepts of warp, loop optimization, and network stack are introduced. In order to improve the optimization performance of the algorithm, three subnetwork structures and stack models are designed, and various methods are used to improve the prediction accuracy of distance education quality assessment. In order to improve the accuracy and timeliness of education quality assessment, this paper proposes a distance education quality assessment model based on mining algorithms. The prediction index is selected by the improved BP neural network. It is required to establish the input layer node as the input vector based on the number of data sources since the input layer is used for data input. The neural network is trained with a quarter of the mining data, and the mining algorithm is further trained with network error trials. A fuzzy relationship matrix is created based on the assessment of teaching quality's hierarchical structure. This leads to the conclusion of the fuzzy thorough evaluation of the effectiveness of distant learning. Experiments show that the proposed model has an average accuracy of 96%, the average teaching quality modeling time is 25.44 ms, and the evaluation speed is fast.

Neighborhood Geometric Structure-Preserving Variational Autoencoder for Smooth and Bounded Data Sources.

Many data sources, such as human poses, lie on low-dimensional manifolds that are smooth and bounded. Learning low-dimensional representations for such data is an important problem. One typical solution is to utilize encoder-decoder networks. However, due to the lack of effective regularization in latent space, the learned representations usually do not preserve the essential data relations. For example, adjacent video frames in a sequence may be encoded into very different zones across the latent space with holes in between. This is problematic for many tasks such as denoising because slightly perturbed data have the risk of being encoded into very different latent variables, leaving output unpredictable. To resolve this problem, we first propose a neighborhood geometric structure-preserving variational autoencoder (SP-VAE), which not only maximizes the evidence lower bound but also encourages latent variables to preserve their structures as in ambient space. Then, we learn a set of small surfaces to approximately bound the learned manifold to deal with holes in latent space. We extensively validate the properties of our approach by reconstruction, denoising, and random image generation experiments on a number of data sources, including synthetic Swiss roll, human pose sequences, and facial expression images. The experimental results show that our approach learns more smooth manifolds than the baselines. We also apply our approach to the tasks of human pose refinement and facial expression image interpolation where it gets better results than the baselines.

Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence

Today, a growing number of computational aids and simulations are shaping model-informed drug development. Artificial intelligence, a family of self-learning algorithms, is only the latest emerging trend applied by academic researchers and the pharmaceutical industry. Nanomedicine successfully conquered several niche markets and offers a wide variety of innovative drug delivery strategies. Still, only a small number of patients benefit from these advanced treatments, and the number of data sources is very limited. As a consequence, “big data” approaches are not always feasible and smart combinations of human and artificial intelligence define the research landscape. These methodologies will potentially transform the future of nanomedicine and define new challenges and limitations of machine learning in their development. In our review, we present an overview of modeling and artificial intelligence applications in the development and manufacture of nanomedicines. Also, we elucidate the role of each method as a facilitator of breakthroughs and highlight important limitations.

The Spirit of Entrepreneurship and the Obstacles Faced by SMPN Students of Banda Aceh in Learning Entrepreneurship

The purpose of this research was to find out how the quality of entrepreneurship learning materials affects students’ entrepreneurial spirit and the obstacles faced in building the entrepreneurial spirit amongthe students of State Junior High School (SMPN) in Banda Aceh City. To obtain maximum results, this research used two approaches, namely quantitative and qualitative approaches. A quantitative approach was used to measure the material and the effect of learning on students' enthusiasm after attending the lesson. Meanwhile, a qualitative approach was used to find accurate data about the obstacles faced in increasing the entrepreneurial spirit of students. Considering that the population was quite large, the data was taken from only 6 public junior high schools with a total population of 1400 people.The sample was taken using the purposive-sampling system.According to the specified sampling formula, the number of data sources was 149 students. The results of the study showed that the learning materials were not satisfactory so that they were unable to influence the students' spirit of entrepreneurship.There were some basic obstacles in learning entrepreneurship material which became one of the factors for the low entrepreneurial spirit of junior high school students in Banda Aceh City. Keywords: learning, entrepreneurial spirit, obstacles

Pengaruh Pelatihan, Kompetensi Dan Motivasi Terhadap Kinerja Pegawai Primkop Kartika Ardagusema Pussenarmed Cimahi

The source of research data is Kartika Cooperative Ardagusema Pussenarmed Primary Staff of Cimahi City Cimahi. The number of data sources is 192 people. With research data on training, competence, motivation on performance. Based on the conclusions of this study, the implications of the training representations included in the sufficient categories. The implication is in management training and improvement in management training. The activity, discussed the stages of formation, transition, core activities, and termination. This causes an increase in the level of participation, unable to control themselves, adjust to the environment, and develop optimal potential so that it does not produce independence in his life at work. Based on the findings in the study, delivered; 1) If the higher the competency, the more energy, facilities, infrastructure, use, methods, media, and financing, the client's tendency to develop its potential optimally the faster it gets independence at work; 2) If the more qualified the training, the more personnel, facilities, needs, methods, and learning media, then the tendency to increase participation is more active in participating in competency improvement activities, and 3) If more qualified, supported by the support provided, then the training is sufficient, and competency is getting better too.

The system of indicators of behavioral risk factors in Russia at the national and regional levels

Introduction. Behavioral risk factors are associated with 47% of all-cause mortality in Russia.The goal of this study was to assess and to select the available data sources on the burden of behavioral risk factors in Russia and its regions.Methods included collection and systematization of medical, demographic and survey which reflected the burden of behavioral risk factors in Russia.Results. The spectrum of sources of the data on behavioral risk factors among adults in Russia has significantly expanded in recent years. Rosstat agency provided regional survey data on prevalence of consumption of tobacco and other nicotine-containing products, physical activity, overweight and obesity. The survey results may underestimate alcohol and tobacco consumption due to the sensitivity of these questions. Therefore, the use of morbidity and mortality data is needed well. Detailed regional data on food product consumption in Russia are available, but the list of food groups needs to be updated to reflect current scientific knowledge. At the same time, there is no monitoring of the consumption of table salt and iodine by the Russian citizens. The data from Rosstat surveys on physical activity is available by the regions, and it provides more reliable results in comparison with the reports of sports organizations. Monitoring of behavioral risk factors among children and adolescents in Russia has yet to be organized.Conclusions. A number of data sources have been identified that can be used to assess the burden of behavioral risk factors in Russia and the regions, and ways to improve such monitoring are proposed.

Implementation of Effective Communication For The Preparation of Oral Presentation Nursing Students at STIKES Bina Sehat PPNI Mojokerto

Oral presentation preparation material as one of the materials to hone speaking skills is very important to be mastered by students. The objectives of this study were: 1) To describe the effective communication preparations for the oral presentation preparation material for the Nursing Undergraduate students of STIKES Bina Sehat PPNI Mojokerto. 2) Describe the results of the implementation of effective communication of the oral presentation preparation material for the Nursing Undergraduate students of STIKES Bina Sehat PPNI Mojokerto. This research method uses qualitative research methods with the number of data sources as many as 40 undergraduate nursing students. The data collection technique used by the researcher was the assignment sheet. The results of this study indicate that all students are able to make effective communication preparations with interesting and actual topics, and are able to apply effective communication stages well

A Bottom-Up Tree Based Storage Approach for Efficient IoT Data Analytics in Cloud Systems

Internet of Things (IoT) has been widely applied in various domains, e.g. environmental monitoring, intelligent transport system, video surveillance, etc. In most of the IoT applications, the IoT data is generated from a number of data sources, not just only one source. In addition, IoT data has various types with different processing requirements. The high-priority IoT data should have better storage and processing manners than the low-priority IoT data. The objective of this paper is to propose an efficient cloud storage approach for considering the multi-aspect requirements of IoT data. In the approach, a light-weight data structure is used to depict the distribution and calculate the size of each IoT subset (type) in all data sources. Then, we form a number of storage-locality groups from cloud storage blocks. However, the storage-locality groups have different storage sizes and locality capabilities. We would like to place the high-priority IoT subset in the storage-locality group with a strong locality capability. Therefore, there is the placement-combinational problem between IoT subsets and the storage-locality groups. To efficiently solve the IoT placement problem, we propose a bottom-up tree based approach associated with the solution of the well-known combinatorial problem: knapsack. Considering the knapsack problem with the NP-hard computational complexity, we also propose a heuristic placement approach.

A Flexible and Robust Deep Learning-Based System for Solar Irradiance Forecasting

Most studies about the solar forecasting topic do not analyze and exploit the temporal and spatial components that are inherent to such a task. Furthermore, they mostly focus just on precision and not on other meaningful features, such as flexibility and robustness. With the current energy production trends, where many solar panels are distributed across city rooftops, there is a need to manage all this information simultaneously and to be able to add and remove sensors as needed. Likewise, robust models need to be able to cope with (inevitable) sensor failure and continue producing reliable predictions. Due to all of this, solar forecasting models need to be as decoupled as possible from the number of data sources that feed them and their geographical distribution, enabling also the reusability of the models. This article contributes with a family of Deep Learning models for solar irradiance forecasting complying with the aforementioned features, i.e. flexibility and robustness. In the first stage, several Artificial Neural Networks are trained as a basis for predicting solar irradiance on several locations at the same time. Thereupon, a family of models that work with irradiance maps thanks to Convolutional Long Short-Term Memory layers is presented, obtaining forecast skills between 7.4% and 41% (depending on the location and horizon) compared to the baseline. The latter family comes with flexibility and robustness features, which are required in large-scale Intelligent Environments, such as Smart Cities. Working with irradiance maps means that new sensors can be added (or removed) as needed, without requiring rebuilding the model. Experiments carried out show that sensor failures have a mild impact on the prediction error for several forecast horizons.

Strategies for Assembling the Biodiversity Knowledge Graph

This talk explores different strategies for assembling the “biodiversity knowledge graph” (Page 2016). The first is a centralised, crowd-sourced approach using Wikidata as the foundation. Wikidata is becoming increasingly attractive as a knowledge graph for the life sciences (Waagmeester et al. 2020), and I will discuss some of its strengths and limitations, particularly as a source of bibliographic and taxonomic information. For example, Wikidata’s handling of taxonomy is somewhat problematic given the lack of clear separation of taxa and their names. A second approach is to build biodiversity knowledge graphs from scratch, such as OpenBioDiv (Penev et al. 2019) and my own Ozymandias (Page 2019). These approaches use either generalised vocabularies such as schema.org, or domain specific ones such as TaxPub (Catapano 2010) and the Semantic Publishing and Referencing Ontologies (SPAR) (Peroni and Shotton 2018), and to date tend to have restricted focus, whether geographic (e.g., Australian animals in Ozymandias) or temporal (recent taxonomic literature, OpenBioDiv). A growing number of data sources are now using schema.org to describe their data, including ORCID and Zenodo, and efforts to extend schema.org into biology (Bioschemas) suggest we may soon be able to build comprehensive knowledge graphs using just schema.org and its derivatives. A third approach is not to build an entire knowledge graph, but instead focus on constructing small pieces of the graph tightly linked to supporting evidence, for example via annotations. Annotations are increasingly used to mark up both the biomedical literature (e.g., Kim et al. 2015, Venkatesan et al. 2017) and the biodiversity literature (Batista-Navarro et al. 2017). One could argue that taxonomic databases are essentially lists of annotations (“this name appears in this publication on this page”), which suggests we could link literature projects such as the Biodiversity Heritage Library (BHL) to taxonomic databases via annotations. Given that the International Image Interoperability Framework (IIIF) provides a framework for treating publications themselves as a set of annotations (e.g., page images) upon which other annotations can be added (Zundert 2018), this suggests ways that knowledge graphs could lead directly to visualising the links between taxonomy and the taxonomic literature. All three approaches will be discussed, accompanied by working examples.

Decision-making Technology Based on Big Data

The paper investigates the issues of building decision support systems based on Big Data. The emphasis is on solving a number of relevant problems, including delayed decision-making, unification of system architectures, and reducing the cost of their development. It is shown that the main reason for the delay is a large number of data sources and a massive amount of information required for synthesizing solutions presented in different languages. For a comprehensive solution to the lag problem, a unified multi-agent architecture and software system are proposed, which, at the user’s request, automatically analyzes text in different languages from thousands of heterogeneous sources in different countries and synthesizes a relevant result to the query. An example of using the system to for decision making on the purchase of goods based on an analysis of customer opinions in different countries is presented.

Adverse Drug Reaction Case Safety Practices in Large Biopharmaceutical Organizations from 2007 to 2017: An Industry Survey.

Drug safety remains a top global public health concern. An increase in the number of data sources available has increased the complexity of pharmacovigilance operations, so the US FDA has created draft guidance focusing on optimizing drug safety data for well-characterized medicines. However, to date, no data demonstrating changes in reports have been presented. This study provided data assessing changes in individual case safety reports (ICSRs) and aggregate reports (ARs) for large biopharmaceutical companies from 2007 to 2017. This study also evaluated current trends on the use of advanced machine and deep learning in order to process all data captured for ICSRs as well as opinions from industry thought leaders on creating a sustainable case-processing operation. Using data captured from Navitas Life Science's annual pvnet® benchmark, we calculated workload indicators characterizing pharmacovigilance operations for large biopharmaceutical organizations. Workload indicators included the number of ICSRs by organization, the number of ARs, and the number and types of data sources used. We also conducted structured in-depth interviews with seven biopharmaceutical executives to discover the reasons for changes in workload indicators across time as well as current strategies for increasing efficiencies in drug safety reporting. The median number of ICSRs increased from 84,960 cases in 2007 to over 200,000 cases in 2017; this increase was largely attributable to an increase in both nonserious cases and follow-up cases. Member companies reported using 12 ± 3 data sources for case identification. The number of ARs also increased from a median of 70 reports in 2007 to 258 reports in 2017. To address these increases, 61% of the biopharmaceutical organizations we surveyed planned to adopt machine learning for full ICSR processing; however, as of 2018, none of the organizations surveyed had mechanisms in place. This study demonstrated that pharmacovigilance departments are currently burdened by ever-increasing case volumes. With increased guidance from regulatory agencies, as well as improvements in artificial intelligence and natural language processing, biopharmaceutical organizations must determine the most resource-efficient and sustainable methods to process the growing volume of cases.

Streaming Big Data Analytics- Current Status, Challenges and Connection of unbounded data Processing platforms

A strategy of examining immense dimensions of structured, un-structured, Semi-Structured data sets is referred as Big data Analytics. Streaming Big Data refers to data generated continuously from number of data sources like Internet-of-Things (IoT) devices, mobile applications, Embedded Sensors, web clicks and many more are needed to be store, processed and analyzed in a tiny interval of time in order to extract meaningful insights and take proper decisions in a timely fashion as the necessity arises. However analyzing streaming big data (continuous flow or unbounded data) is a very challenging problem. Continuous data streams have become essential prerequisite for numerous industrial and scientific applications, the current existing technology Hadoop-MapReduce is not appropriate for stream processing of big data. This paper discusses the challenges and benefits of streaming big data along with its architecture, and focuses on different open source streaming processing platforms that are existed to process the huge data at a high speed