Journal articles on the topic 'Large Scale Applications Implementing'
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Honea, Rosemary, and Bonnie Mensch. "Maintaining continuity of clinical operations while implementing large-scale filmless operations." Journal of Digital Imaging 12, S1 (May 1999): 50–53. http://dx.doi.org/10.1007/bf03168754.
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Dong, Biao. "Architecting Large Scale Wireless Sensor Networks Publish/Subscribe Applications: A Graph-Oriented Approach." Applied Mechanics and Materials 321-324 (June 2013): 2768–71. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.2768.
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This paper presented a approach, called GOHM, for modeling and implementing the architecture of large scale wireless sensor networks(LSWSNs) publish/subscribe(Pub/Sub) applications using a graph-oriented hierarchical model(GOHM). Considering the scalability of topology which was necessary in the LSWSNs, a sparse hierarchical graph(SHG) was defined based on the hierarchical topology model. The simulation indicated that SHG provided low mean-latency at high throughput for transmitting messages. The results imply that SHG can easily be constructed, while ensuring good scalability.
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Stephen Dass A. and Prabhu J. "Ameliorating the Privacy on Large Scale Aviation Dataset by Implementing MapReduce Multidimensional Hybrid k-Anonymization." International Journal of Web Portals 11, no. 2 (July 2019): 14–40. http://dx.doi.org/10.4018/ijwp.2019070102.
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In this fast growing data universe, data generation and data storage are moving into the next-generation process by generating petabytes and gigabytes in an hour. This leads to data accumulation where privacy and preservation are certainly misplaced. This data contains some sensitive and high privacy data which is to be hidden or removed using hashing or anonymization algorithms. In this article, the authors propose a hybrid k anonymity algorithm to handle large scale aircraft datasets with combined concepts of Big Data analytics and privacy preservation of storing the dataset with the help of MapReduce. This published anonymized data are moved by MapReduce to the Hive database for data storage. The authors propose a multi-dimensional hybrid k-anonymity technique to solve the privacy issue and compare the proposed system with other two anonymization methods such as BUG and TDS. Three experiments were performed for evaluating classifier error, calculating disruption value and p% hybrid anonymity and estimation of processing time.
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Mishra, Nilamadhab, Chung-Chih Lin, and Hsien-Tsung Chang. "A Cognitive Adopted Framework for IoT Big-Data Management and Knowledge Discovery Prospective." International Journal of Distributed Sensor Networks 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/718390.
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In future IoT big-data management and knowledge discovery for large scale industrial automation application, the importance of industrial internet is increasing day by day. Several diversified technologies such as IoT (Internet of Things), computational intelligence, machine type communication, big-data, and sensor technology can be incorporated together to improve the data management and knowledge discovery efficiency of large scale automation applications. So in this work, we need to propose a Cognitive Oriented IoT Big-data Framework (COIB-framework) along with implementation architecture, IoT big-data layering architecture, and data organization and knowledge exploration subsystem for effective data management and knowledge discovery that is well-suited with the large scale industrial automation applications. The discussion and analysis show that the proposed framework and architectures create a reasonable solution in implementing IoT big-data based smart industrial applications.
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Kareekunnan, Afsal, Tatsufumi Agari, Takeshi Kudo, Shunsuke Niwa, Yoshito Abe, Takeshi Maruyama, Hiroshi Mizuta, and Manoharan Muruganathan. "Graphene electric field sensor for large scale lightning detection network." AIP Advances 12, no. 9 (September 1, 2022): 095209. http://dx.doi.org/10.1063/5.0095449.
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Graphene is widely used in various real-life applications due to its high sensitivity to the change in the carrier concentration. Here, we demonstrate that graphene can be used for implementing a reliable lightning detection network as it shows excellent sensitivity to the electric field of both positive and negative polarities, with a wide range of magnitude. The lowest electric field detected by our graphene sensor is 67 V/m, which is much smaller than the detection limit of previously reported graphene sensors and comparable to that of field mill and MEMS-based sensors. We also present the results of outdoor experiments where the response of the graphene sensor to the atmospheric electric field on a lightning day was tested and found to be in good agreement with the existing field mill sensor.
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Lapidus, Azariy, and Ivan Abramov. "Implementing large-scale construction projects through application of the systematic and integrated method." IOP Conference Series: Materials Science and Engineering 365 (June 2018): 062002. http://dx.doi.org/10.1088/1757-899x/365/6/062002.
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Amanda Hickey, Margaret Henning, and Lissa Sirois. "Lessons Learned During Large-Scale Implementation Project Focused on Workplace Lactation Practices and Policies." American Journal of Health Promotion 36, no. 3 (November 20, 2021): 477–86. http://dx.doi.org/10.1177/08901171211055692.
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Purpose This practice-based research funded by the Centers for Disease Control and Prevention (CDC) focuses on the translation of evidence-based practices and policies into real-world applications. To the best of our knowledge, this is the largest study to research the implementation process for lactation accommodations and policies for work sites. Design or Approach Pre-/post-test evaluation of work-site lactation accommodations, and 6-month follow-up with business that worked on the project. Setting/Participants 34 businesses across New Hampshire. Method The team developed work-site selection criteria to award mini-grants; developed trainings and a toolkit; and worked with 34 businesses over a 3-year period. Pre-/post-implementation data were collected using the CDC work-site scorecard. A 6-month follow-up phone interview was conducted with each site. Results We assessed the CDC scorecard and evaluated the challenges of implementing lactation spaces by industry. In our 6-month follow-up, we found that spaces were still being utilized and we identified specific research to inform practical evidence-based applications and lessons learned when implementing a work-site lactation space. Conclusion We successfully provided financial/technical support to develop or improve 45 lactation spaces, with policies and practices to support mothers and families for 34 businesses. We identified key takeaway lessons that can be used to guide the development of lactation spaces and policies in work sites. Sites self-report that these work-site changes were sustainable at 6-month follow-up.
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Ortt, Roland, Claire Stolwijk, and Matthijs Punter. "Implementing Industry 4.0: assessing the current state." Journal of Manufacturing Technology Management 31, no. 5 (August 10, 2020): 825–36. http://dx.doi.org/10.1108/jmtm-07-2020-0284.
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PurposeThe purpose of this paper is to introduce, summarize and combine the results of 11 articles in a special issue on the implementation of Industry 4.0. Industry 4.0 emerged as a phenomenon about a decade ago. That is why, it is interesting now to explore the implementation of the concept. In doing so, four research questions are addressed: (1) What is Industry 4.0? (2) How to implement Industry 4.0? (3) How to assess the implementation status of Industry 4.0? (4) What is the current implementation status of Industry 4.0?Design/methodology/approachSubgroups of articles are formed, around one or more research questions involving the implementation of Industry 4.0. The articles are carefully analyzed to provide comprehensive answers.FindingsBy comparing definitions systematically, the authors show important aspects for defining Industry 4.0. The articles in the special issue explore several cases of manufacturing companies that implemented Industry 4.0. In addition, systematic approaches to aid implementation are described: an approach to combine case-study results to solve new implementation problems, approaches to assess readiness or maturity of companies regarding Industry 4.0 and surveys showing the status of implementation in larger samples of companies as well as showing relationships between company characteristics and type of implementation. Small and large firms differ considerably in their process of implementing Industry 4.0, for example.Research limitations/implicationsThis special issue discusses implementation of Industry 4.0. The issue is limited to 11 articles, each of which with its own strengths and limitations.Practical implicationsThe practical relevance of the issue is that it focuses on the implementation of Industry 4.0. Cases showing successful implementation, measurement instruments to assess degree of implementation and advice how to build a database with cases together with large-scale studies on the state of implementation do provide a wealth of information with a large managerial relevance.Originality/valueThe paper introduces an original take on Industry 4.0 by focusing on implementation. The special issue contains both literature reviews, articles describing case studies of implementation, articles developing systematic measurement instruments to assess degree of implementation and some articles reporting large-scale studies on the state of implementation of Industry 4.0 and thereby combine several perspectives on implementation of Industry 4.0.
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Gilpin, William. "Cryptographic hashing using chaotic hydrodynamics." Proceedings of the National Academy of Sciences 115, no. 19 (April 23, 2018): 4869–74. http://dx.doi.org/10.1073/pnas.1721852115.
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Fluids may store and manipulate information, enabling complex applications ranging from digital logic gates to algorithmic self-assembly. While controllable hydrodynamic chaos has previously been observed in viscous fluids and harnessed for efficient mixing, its application to the manipulation of digital information has been sparsely investigated. We show that chaotic stirring of a viscous fluid naturally produces a characteristic signature of the stirring process in the arrangement of particles in the fluid, and that this signature directly satisfies the requirements for a cryptographic hash function. This includes strong divergence between similar stirring protocols’ hashes and avoidance of collisions (identical hashes from distinct stirs), which are facilitated by noninvertibility and a broad chaotic attractor that samples many points in the fluid domain. The hashing ability of the chaotic fluidic map implicates several unexpected mechanisms, including incomplete mixing at short time scales that produces a hyperuniform hash distribution. We investigate the dynamics of hashing using interparticle winding statistics, and find that hashing starts with large-scale winding of kinetically disjoint regions of the chaotic attractor, which gradually gives way to smaller scale braiding of single-particle trajectories. In addition to providing a physically motivated approach to implementing and analyzing deterministic chaotic maps for cryptographic applications, we anticipate that our approach has applications in microfluidic proof-of-work systems and characterizing large-scale turbulent flows from sparse tracer data.
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Andre, Walder. "Efficient adaptation of the Karatsuba algorithm for implementing on FPGA very large scale multipliers for cryptographic algorithms." International Journal of Reconfigurable and Embedded Systems (IJRES) 9, no. 3 (November 1, 2020): 235. http://dx.doi.org/10.11591/ijres.v9.i3.pp235-241.
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<span lang="EN-US">Here, we present a modified version of the Karatsuba algorithm to facilitate the FPGA-based implementation of three signed multipliers: 32-bit × 32-bit, 128-bit x 128-bit, and 512-bit × 512-bit. We also implement the conventional 32-bit × 32-bit multiplier for comparative purposes. The Karatsuba algorithm is preferable for multiplications with very large operands such as 64-bit × 64-bit, 128-bit × 128-bit, 256-bit × 256-bit, 512-bit × 512-bit multipliers and up. Experimental results show that the Karatsuba multiplier uses less hardware in the FPGA compared to the conventional multiplier. The Xilinx xc7k325tfbg900 FPGA using the Genesis 2 development board is used to implement the proposed scheme. The results obtained are promising for applications that require rapid implementation and reconfiguration of cryptographic algorithms.</span>Here, we present a modified version of the Karatsuba algorithm to facilitate the FPGA-based implementation of three signed multipliers: 32-bit × 32-bit, 128-bit x 128-bit, and 512-bit × 512-bit. We also implement the conventional 32-bit × 32-bit multiplier for comparative purposes. The Karatsuba algorithm is preferable for multiplications with very large operands such as 64-bit × 64-bit, 128-bit × 128-bit, 256-bit × 256-bit, 512-bit × 512-bit multipliers and up. Experimental results show that the Karatsuba multiplier uses less hardware in the FPGA compared to the conventional multiplier. The Xilinx xc7k325tfbg900 FPGA using the Genesis 2 development board is used to implement the proposed scheme. The results obtained are promising for applications that require rapid implementation and reconfiguration of cryptographic algorithms.
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Ferdaous, Jenhani, and Mohamed Salah Gouider. "Large-Scale System for Social Media Data Warehousing." International Journal of Data Warehousing and Mining 18, no. 1 (January 2022): 1–18. http://dx.doi.org/10.4018/ijdwm.290890.
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Social media data become an integral part in the business data and should be integrated into the decisional process for better decision making based on information which reflects better the true situation of business in any field. However, social media data are unstructured and generated in very high frequency which exceeds the capacity of the data warehouse. In this work, we propose to extend the data warehousing process with a staging area which heart is a large scale system implementing an information extraction process using Storm and Hadoop frameworks to better manage their volume and frequency. Concerning structured information extraction, mainly events, we combine a set of techniques from NLP, linguistic rules and machine learning to succeed the task. Finally, we propose the adequate data warehouse conceptual model for events modeling and integration with enterprise data warehouse using an intermediate table called Bridge table. For application and experiments, we focus on drug abuse events extraction from Twitter data and their modeling into the Event Data Warehouse.
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Kandylas, Vasilis, Omar Alonso, Shiroy Choksey, Kedar Rudre, and Prashant Jaiswal. "Automating Crowdsourcing Tasks in an Industrial Environment." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 1 (November 3, 2013): 95–96. http://dx.doi.org/10.1609/hcomp.v1i1.13056.
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Crowdsourcing based applications are starting to gain traction in industrial environments. Crowdsourcing research has proven that it is possible to get good quality labels at the fraction of the cost and time. However, implementing such applications at large scale requires new infrastructure. In this demo we present a system that allows the automation of crowdsourcing tasks for information retrieval experiments.
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Wagner, Wolfgang, Bernhard Bauer-Marschallinger, Claudio Navacchi, Felix Reuß, Senmao Cao, Christoph Reimer, Matthias Schramm, and Christian Briese. "A Sentinel-1 Backscatter Datacube for Global Land Monitoring Applications." Remote Sensing 13, no. 22 (November 17, 2021): 4622. http://dx.doi.org/10.3390/rs13224622.
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The Sentinel-1 Synthetic Aperture Radar (SAR) satellites allow global monitoring of the Earth’s land surface with unprecedented spatio-temporal coverage. Yet, implementing large-scale monitoring capabilities is a challenging task given the large volume of data from Sentinel-1 and the complex algorithms needed to convert the SAR intensity data into higher-level geophysical data products. While on-demand processing solutions have been proposed to cope with the petabyte-scale data volumes, in practice many applications require preprocessed datacubes that permit fast access to multi-year time series and image stacks. To serve near-real-time as well as offline land monitoring applications, we have created a Sentinel-1 backscatter datacube for all continents (except Antarctica) that is constantly being updated and maintained to ensure consistency and completeness of the data record over time. In this technical note, we present the technical specifications of the datacube, means of access and analysis capabilities, and its use in scientific and operational applications.
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Han, Chun Li, and Yun Chao Ma. "Design and Development of Large-Scale Sport Events Information System - Based on Project Management." Advanced Materials Research 219-220 (March 2011): 1195–200. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.1195.
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In order to effectively implementing the organization and management of large-scale sport events, and carrying out the important role of project management during the events, by integrating the use of research methods such as system science, management science, information technology, we designed a information system for large-scale sport events based on project management. In this paper we systematically discusses the characteristics of large-scale sport events based on project management, explores the application goal of information system for large-scale sport events based on project management, and on this basis we elaborates comprehensively on the system’s functional requirements, framework, functional structure, and other major issues during the execution.
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Ma, Yan, Jie Song, and Zhixin Zhang. "In-Memory Distributed Mosaicking for Large-Scale Remote Sensing Applications with Geo-Gridded Data Staging on Alluxio." Remote Sensing 14, no. 23 (November 25, 2022): 5987. http://dx.doi.org/10.3390/rs14235987.
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The unprecedented availability of petascale analysis-ready earth observation data has given rise to a remarkable surge in demand for regional to global environmental studies, which exploit tons of data for temporal–spatial analysis at a much larger scale than ever. Imagery mosaicking, which is critical for forming “One Map” with a continuous view for large-scale climate research, has drawn significant concern. However, despite employing distributed data processing engines such as Spark, large-scale data mosaicking still significantly suffers from a staggering number of remote sensing images which could inevitably lead to discouraging performance. The main ill-posed problem of traditional parallel mosaicking algorithms is inherent in the huge computation demand and incredible heavy data I/O burden resulting from intensively shifting tremendous RS data back and forth between limited local memory and bulk external storage throughout the multiple processing stages. To address these issues, we propose an in-memory Spark-enabled distributed data mosaicking at a large scale with geo-gridded data staging accelerated by Alluxio. It organizes enormous “messy” remote sensing datasets into geo-encoded gird groups and indexes them with multi-dimensional space-filling curves geo-encoding assisted by GeoTrellis. All the buckets of geo-grided remote sensing data groups could be loaded directly from Alluxio with data prefetching and expressed as RDDs implemented concurrently as grid tasks of mosaicking on top of the Spark-enabled cluster. It is worth noticing that an in-memory data orchestration is offered to facilitate in-memory big data staging among multiple mosaicking processing stages to eliminate the tremendous data transferring at a great extent while maintaining a better data locality. As a result, benefiting from parallel processing with distributed data prefetching and in-memory data staging, this is a much more effective approach to facilitate large-scale data mosaicking in the context of big data. Experimental results have demonstrated our approach is much more efficient and scalable than the traditional ways of parallel implementing.
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Freytag, Saskia, and Ryan Lister. "schex avoids overplotting for large single-cell RNA-sequencing datasets." Bioinformatics 36, no. 7 (December 3, 2019): 2291–92. http://dx.doi.org/10.1093/bioinformatics/btz907.
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Abstract Summary Due to the scale and sparsity of single-cell RNA-sequencing data, traditional plots can obscure vital information. Our R package schex overcomes this by implementing hexagonal binning, which has the additional advantages of improving speed and reducing storage for resulting plots. Availability and implementation schex is freely available from Bioconductor via http://bioconductor.org/packages/release/bioc/html/schex.html and its development version can be accessed on GitHub via https://github.com/SaskiaFreytag/schex. Supplementary information Supplementary data are available at Bioinformatics online.
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Chae, Bongsug, and Giovan Francesco Lanzara. "Self‐destructive dynamics in large‐scale technochange and some ways of counteracting it." Information Technology & People 19, no. 1 (January 1, 2006): 74–97. http://dx.doi.org/10.1108/09593840610649970.
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PurposeSeeks to raise the question of why large‐scale technochange is difficult and often failure‐prone and to attempt to answer this question by viewing technochange as an instance of institutional change and design in which self‐destructive mechanisms are inherently embedded.Design/methodology/approachIn order to explore the complex institutional dynamics of large‐scale technochange the paper uses the exploration/exploitation framework originally developed by March and extended by Lanzara to the study of institution‐building processes in the political domain. The argument is that problems in implementing large‐scale technochange stem from learning dilemmas in the inter‐temporal and inter‐group allocation of material and cognitive resources. The paper uses a case of large‐scale technology in a major US university system to illustrate the institutional perspective on technochange.FindingsIt is argued and illustrated that the development and redesign of large‐scale information systems involve both the exploration of alternative institutional arrangements and the exploitation of pre‐existing ones, such that a delicate balance must be struck to overcome incoherences and dilemmas between the two activities.Research limitations/implicationsThe proposed framework to understand large‐scale technochange is not examined empirically. The illustration of the framework relies on a single large‐scale system project of a non‐profit organization in the USA. Further empirical work and comparative research on multiple cases are needed.Practical implicationsThe paper discusses some sources of the failures of large‐scale technochange and offers three interrelated mechanisms to counteract such failure sources, namely focal points, increasing returns, and bricolage. These counteracting mechanisms may help organizations to effectively deal with the dilemmas of exploration and exploitation in technochange.Originality/valueThis paper fills the gap in understanding the nature of large‐scale technochange, providing an explanation of why it is difficult and failure‐prone and offering some modest proposals for intervention in large‐scale system projects.
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Swapnil, Raj, and Kumar Raghav Anuj. "Elasticity in the cloud related to database autonomies and scalability." i-manager’s Journal on Cloud Computing 9, no. 1 (2022): 26. http://dx.doi.org/10.26634/jcc.9.1.18719.
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Cloud computing has been a very popular paradigm for implementing online applications. Scalability, elasticity, cost of use, and large-scale economies are the main reasons for the effective and widespread acceptance of cloud computing. In this paper, we outline our work to inject the aforementioned "cloud capabilities" into a database system designed to support various applications deployed in the cloud: designing scalable databases using autonomies database and elasticity that enables lightweight resiliency using low-cost live database migrations and an intelligent and autonomous controller designed for system management without human intervention.
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Chen, Mengxing, Shih-Feng Chou, Frede Blaabjerg, and Pooya Davari. "Overview of Power Electronic Converter Topologies Enabling Large-Scale Hydrogen Production via Water Electrolysis." Applied Sciences 12, no. 4 (February 11, 2022): 1906. http://dx.doi.org/10.3390/app12041906.
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Renewable power-to-hydrogen (P2H) technology is one of the most promising solutions for fulfilling the increasing global demand for hydrogen and to buffer large-scale, fluctuating renewable energies. The high-power, high-current ac/dc converter plays a crucial role in P2H facilities, transforming medium-voltage (MV) ac power to a large dc current to supply hydrogen electrolyzers. This work introduces the general requirements, and overviews several power converter topologies for P2H systems. The performances of different topologies are evaluated and compared from multiple perspectives. Moreover, the future trend of eliminating the line frequency transformer (LFT) is discussed. This work can provide guidance for future designing and implementing of power-electronics-based P2H systems.
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Matějásko, Michal, Martin Brablc, Martin Appel, and Robert Grepl. "Contactless Fault Detection of a DC Motor Direction of Rotation Using Its Stray Magnetic Field." Machines 9, no. 11 (November 10, 2021): 281. http://dx.doi.org/10.3390/machines9110281.
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In large-scale manufacturing and assembly applications, especially when trying to automate most steps, implementing quality control as early in the process as possible is the key to prevent expenses later. We deal mainly with the production of DC motor powered fuel pumps, which are commonly used in the automotive industry. The goal of this paper is to present a newly developed technique for non-invasive fault detection of a DC motor’s direction of rotation using a stray magnetic field out of the motor chassis. The results presented in this paper show that it is possible to detect faults even on low-power motors while the algorithm is kept as simple as possible to allow for large-scale deployment on a production line. It also gives new insight into the behavior of the stray magnetic field of electric motors, which may benefit other applications and future research.
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Saravana, B. Shanthi, and V. Rajini. "Application of Wind Energy Source to Large Scale Desalination System Prefeasibility Analysis-Investigation of Wind Power Potential in Coastal Areas of Tamilnadu." Advanced Materials Research 768 (September 2013): 57–63. http://dx.doi.org/10.4028/www.scientific.net/amr.768.57.
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The total power demand in our country is increasing every year because of the increasing population. Conventional energy sources are limited by the constraints such as emission of CO2, increasing price of oil etc. To increase the power generation we need to go for non conventional energy sources among which wind energy is available in abundance. This paper discusses the possibility of using wind energy for the stand alone large scale applications eliminating the constraints on the grid connection. This is the pre feasibility study of wind power generation by analyzing the daily wind patterns in the coastal areas. This forms a good base for analyzing the feasibility of standalone wind system to large scale applications. HOMER software is used as an optimization tool. From the results Chennai has better energy density and Tutukurin has higher capacity factor. The study makes it feasible to next step in analyzing the feasibility of implementing a standalone desalination unit in coastal regions of Tamilnadu.
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CARRASSI, ALBERTO, and STÉPHANE VANNITSEM. "TREATMENT OF THE ERROR DUE TO UNRESOLVED SCALES IN SEQUENTIAL DATA ASSIMILATION." International Journal of Bifurcation and Chaos 21, no. 12 (December 2011): 3619–26. http://dx.doi.org/10.1142/s0218127411030775.
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In this paper, a method to account for model error due to unresolved scales in sequential data assimilation, is proposed. An equation for the model error covariance required in the extended Kalman filter update is derived along with an approximation suitable for application with large scale dynamics typical in environmental modeling. This approach is tested in the context of a low order chaotic dynamical system. The results show that the filter skill is significantly improved by implementing the proposed scheme for the treatment of the unresolved scales.
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Ghormley, Yvette. "Twos Company, Threes A Cloud: Challenges To Implementing Service Models." Journal of Service Science (JSS) 5, no. 1 (March 28, 2012): 19–28. http://dx.doi.org/10.19030/jss.v5i1.6938.
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Although three models are currently being used in cloud computing (Software as a Service, Platform as a Service, and infrastructure as a service, there remain many challenges before most business accept cloud computing as a reality. Virtualization in cloud computing has many advantages but carries a penalty because of state configurations, kernel drivers, and user interface environments. In addition, many non-standard architectures exist to power cloud models that are often incompatible. Another issue is adequately provisioning the resources required for a multi-tier cloud-based application in such a way that on-demand elasticity is present at vastly different scales yet is carried out efficiently. For networks that have large geographical footprints another problem arises from bottlenecks between elements supporting virtual machines and their control. While many solutions have been proposed to alleviate these problems, some of which are already commercial, much remains to be done to see whether these solutions will be practicable at scale up and address business concerns.
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Yao, Zhewei, Peng Xu, Fred Roosta, and Michael W. Mahoney. "Inexact Nonconvex Newton-Type Methods." INFORMS Journal on Optimization 3, no. 2 (January 2021): 154–82. http://dx.doi.org/10.1287/ijoo.2019.0043.
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The paper aims to extend the theory and application of nonconvex Newton-type methods, namely trust region and cubic regularization, to the settings in which, in addition to the solution of subproblems, the gradient and the Hessian of the objective function are approximated. Using certain conditions on such approximations, the paper establishes optimal worst-case iteration complexities as the exact counterparts. This paper is part of a broader research program on designing, analyzing, and implementing efficient second-order optimization methods for large-scale machine learning applications. The authors were based at UC Berkeley when the idea of the project was conceived. The first two authors were PhD students, the third author was a postdoc, all supervised by the fourth author.
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Bhatt, Sandeep, Marina Chen, James Cowie, Cheng-Yee Lin, and Pangfeng Liu. "Object-Oriented Support for Adaptive Methods on Paranel Machines." Scientific Programming 2, no. 4 (1993): 179–92. http://dx.doi.org/10.1155/1993/474972.
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This article reports on experiments from our ongoing project whose goal is to develop a C++ library which supports adaptive and irregular data structures on distributed memory supercomputers. We demonstrate the use of our abstractions in implementing "tree codes" for large-scale N-body simulations. These algorithms require dynamically evolving treelike data structures, as well as load-balancing, both of which are widely believed to make the application difficult and cumbersome to program for distributed-memory machines. The ease of writing the application code on top of our C++ library abstractions (which themselves are application independent), and the low overhead of the resulting C++ code (over hand-crafted C code) supports our belief that object-oriented approaches are eminently suited to programming distributed-memory machines in a manner that (to the applications programmer) is architecture-independent. Our contribution in parallel programming methodology is to identify and encapsulate general classes of communication and load-balancing strategies useful across applications and MIMD architectures. This article reports experimental results from simulations of half a million particles using multiple methods.
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Cho, Yun-Sung, and Yun-Hyuk Choi. "Methodology for Implementing the State Estimation in Renewable Energy Management Systems." Energies 14, no. 8 (April 19, 2021): 2301. http://dx.doi.org/10.3390/en14082301.
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This paper describes a methodology for implementing the state estimation and enhancing the accuracy in large-scale power systems that partially depend on variable renewable energy resources. To determine the actual states of electricity grids, including those of wind and solar power systems, the proposed state estimation method adopts a fast-decoupled weighted least square approach based on the architecture of application common database. Renewable energy modeling is considered on the basis of the point of data acquisition, the type of renewable energy, and the voltage level of the bus-connected renewable energy. Moreover, the proposed algorithm performs accurate bad data processing using inner and outer functions. The inner function is applied to the largest normalized residue method to process the bad data detection, identification and adjustment. While the outer function is analyzed whether the identified bad measurements exceed the condition of Kirchhoff’s current law. In addition, to decrease the topology and measurement errors associated with transformers, a connectivity model is proposed for transformers that use switching devices, and a transformer error processing technique is proposed using a simple heuristic method. To verify the performance of the proposed methodology, we performed comprehensive tests based on a modified IEEE 18-bus test system and a large-scale power system that utilizes renewable energy.
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Nur, Muhammad, Muhammad Ahsin Rifa'i, Rizmi Yunita, and Leila Ariyani Sofia. "Pemetaan Sebaran Karamba Jaring Apung Berdasarkan Zona Dan Tingkat Skala Usaha Menggunakan Drone Di Waduk Riam Kanan Provinsi Kalimantan Selatan." EnviroScienteae 16, no. 2 (December 8, 2020): 276. http://dx.doi.org/10.20527/es.v16i2.9659.
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Aquaculture is growing rapidly in Banjar District of Kalimantan Selatan Province, including on Riam Kanan Reservoir which is a center for floating net cage aquaculture. The increasing aquaculture on Riam Kanan Reservoir is not supported by adequate and actual data regarding the distribution, number of plots and number of fishery household (FHs) of floating net cage aquaculture. Complete, accurate and actual data is needed as one of the considerations in implementing sustainable aquaculture management policies. This study aims to map the distribution of floating net cages based on zones and business scale level, analyze the number of plots and the number of FHs of floating net cage aquaculture on Riam Kanan Reservoir. The study was conducted in July-October 2019 using 2 survey methods: a drone survey to collect aerial photographs and a field survey to identify the number of FHs and floating net cage plots based on business scale level. The results of aerial photography were analyzed using the Agisoft Metashape Professional and Arcgis 10.7 applications. Field survey data were analyzed using the Archgis 10.7 application and Microsoft Excel. The floating net cage is spread over 6 zones totaling 4,263 plots, consisting of criteria for small business scale 1,774 plots, medium scale 1,234 plots and large scale 1,255 plots. FHs of floating net cage aquaculture spread in 7 villages as many as 425 units with the criteria of 331 small scale business, 87 medium scale and 28 large scale. The plots and FHs of floating net cage aquaculture on Riam Kanan Reservoir has increased significantly over the past 4 years.
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Romagnoli, Francesco, Anton Rayan Priyasad Perera Weerasuriya-Arachchige, Riccardo Paoli, Maksims Feofilovs, and Baiba Ievina. "Growth Kinetic Model for Microalgae Cultivation in Open Raceway Ponds: A System Dynamics Tool." Environmental and Climate Technologies 25, no. 1 (January 1, 2021): 1317–36. http://dx.doi.org/10.2478/rtuect-2021-0100.
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Abstract Microalgae culture has the potential to play an essential role in the application of circular economy principles. Microalgae cultivation allows utilizing industrial side-waste streams while ensuring biomass for a wide range of applications in the industrial sector. Specifically, cultivation in outdoor open raceway ponds are a preferred solution due to low costs, ease of operation and large-scale application. However, the economic viability of the cultivation system largely depends on the amount of biomass produced, the technology implemented and the microalgae species and strains. For this purpose, screening of numerous physical, chemical, and environmental factors affecting microalgae growth must be performed before implementing large-scale microalgae cultivation systems. Furthermore, to obtain the highest biomass yield, the design and operating parameters for open raceway pond cultivation must be investigated in depth. Therefore, this study proposes a kinetic growth model for microalgae cultivation in open raceway ponds based on System Dynamics modelling approach. The proposed model aims at overcoming the major problems of existing growth evaluation tools such as separate assessment of different parameters, high complexity, time consumption and other challenges. The proposed system dynamics model proves to be a simple yet powerful tool for modelling the behaviour of algae biomass in an open raceway pond.
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Bayraktar, H., D. Y. Bayar, B. Kara, and G. Bilgin. "LEVERAGING MATURITY ASSESSMENT TO CHOOSE THE RIGHT APPLICATIONS FOR SMART CITIES: TURKEY’S APPROACH." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-4/W3-2020 (November 23, 2020): 137–42. http://dx.doi.org/10.5194/isprs-archives-xliv-4-w3-2020-137-2020.
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Abstract. Cities are facing numerous challenges because of the unprecedented growth of population all over the world. In this context, smart city stands out as a viable option to improve quality of life. Smart city, with its ability to transform the information into economic, social and environmental benefits, offers acquisitions in the fields of sustainable development, competitiveness and environmental sustainability. However, the cost of implementing and maintaining smart city applications on a large scale reveals the necessity to choose the right smart city application at the beginning of smart city transformation. In order to determine which smart city application should be used in smart city domain, the current situation and needs of the city should be analysed effectively. Maturity assessment can be used as a tool to understand the existing conditions of a city. In this study, Turkey's smart city approach will be addressed and Smart City Maturity Assessment Model of Turkey will be introduced with the preparation and implementation process. Consequently, the impact of the Smart City Maturity Assessment Model on selection of smart city applications will be discussed with the result of maturity assessment which is implemented on 4 cities of Turkey.
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Sun, Jing, and Hong Tao Wang. "Research on Optimization and Application of LOD Algorithm in Virtual Reality Visualization of Terrain." Applied Mechanics and Materials 687-691 (November 2014): 1258–61. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1258.
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With the development of computer graphics, real-time rendering-based VF: technology has been applied in more and more fields. LOD is the key technology in large-scale terrain rendering. In this paper, the basic concept of LOD is introduced briefly and some algorithms of LOD in use are mentioned and analyzed; secondly as one of algorithms of LOD, View-Dependent Progressive Mesh algorithm is studied and improved, the result of implementing the large-scale terrain’s LOD by using VDPM is presented. There are key technologies in LOD Large-scale terrain real-time rendering are researched. Relative technologies are presented such as: LOD of the terrain, visibility culling, and cracks eliminate, view-dependent refine, LOD error, technologies of texture etc. Using LOD technology, VR system can greatly reduce the; number of polygons produced in real-time rendering procedure. Finally, we do experimental design work based on the methods and techniques presented by this paper.
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YANG, TAO, and LEON O. CHUA. "IMPLEMENTING BACK-PROPAGATION- THROUGH-TIME LEARNING ALGORITHM USING CELLULAR NEURAL NETWORKS." International Journal of Bifurcation and Chaos 09, no. 06 (June 1999): 1041–74. http://dx.doi.org/10.1142/s0218127499000730.
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In a programmable (multistage) cellular neural network (CNN) structure, the CPU is a CNN universal chip which supports massively parallel computations on patterns and images, including videos. In this paper, we decompose the structure of a class of simultaneous recurrent networks (SRN) into a CNN program and run it on a von Neumann-like stored program CNN structure. To train the SRN, we map the back-propagation-through-time (BTT) learning algorithm into a sequence of CNN subroutines to achieve real-time performance via a CNN universal chip. By computing in parallel, the CNN universal chip can be programmed to implement in real time the BTT learning algorithm, which has a very high time complexity. An estimate of the time complexity of the BTT learning algorithm based on the CNN universal chip is presented. For small-scale problems, our simulation results show that a CNN implementation of the BTT learning algorithm for a two-dimensional SRN is at least 10,000 times faster than that based on state-of-the-art sequential workstations. For the few large-scale problems which we have so far simulated, the CNN implemented BTT learning algorithm maintained virtually the same time complexity with a learning time of a few seconds, while those implemented on state-of-the-art sequential workstations dramatically increased their time complexity, often requiring several days of running time. Several examples are presented to demonstrate how efficiently a CNN universal chip can speed up the learning algorithm for both off-line and on-line applications.
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Liu, Hai Tao, Hui Yu, Yi Ming Lu, Guang Xian Lv, Yu Chen, and Xin Hui Zhang. "Distributed Energy Resource Impacts on FLISR Scheme in Distribution System." Advanced Materials Research 986-987 (July 2014): 286–90. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.286.
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The key distribution automation (DA) application for implementing a self-healing smart distribution network is the fault location isolation and service restoration (FLISR) application. With DGs introduce significantly more variability into distribution system power flows, it has a great impact to FLISR. Based on summarizing the existing FLISR scheme, this paper analyses the influence of fault detection and service restoration with large-scale DGs.
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Ermolaev, K. A., and S. E. Yormatov. "TECHNOLOGICAL ASPECTS OF IMPLEMENTING THE PRINCIPLES OF DECENTRALIZED IDENTITY IN THE EUROPEAN UNION." Chronos 7, no. 4(66) (June 13, 2022): 127–31. http://dx.doi.org/10.52013/2658-7556-66-4-37.
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The article discusses the key prerequisites and regulatory conditions for the transition to a new model for managing personal data and ensuring cybersecurity in the European Union. Demonstration examples of the implementation of the principles of decentralized identity are analyzed from the standpoint of organizational-role and functional models that predetermine the requirements for the technical and information architecture for their implementation. Based on the results of the analysis, on the one hand, the key drivers of development and the most promising areas of application of decentralized identity technologies were formulated, and on the other hand, technical difficulties that prevent their large-scale application at present.
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Aulicino, Francesco, Julien Capin, and Imre Berger. "Synthetic Virus-Derived Nanosystems (SVNs) for Delivery and Precision Docking of Large Multifunctional DNA Circuitry in Mammalian Cells." Pharmaceutics 12, no. 8 (August 11, 2020): 759. http://dx.doi.org/10.3390/pharmaceutics12080759.
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DNA delivery is at the forefront of current research efforts in gene therapy and synthetic biology. Viral vectors have traditionally dominated the field; however, nonviral delivery systems are increasingly gaining traction. Baculoviruses are arthropod-specific viruses that can be easily engineered and repurposed to accommodate and deliver large sequences of exogenous DNA into mammalian cells, tissues, or ultimately organisms. These synthetic virus-derived nanosystems (SVNs) are safe, readily customized, and can be manufactured at scale. By implementing clustered regularly interspaced palindromic repeats (CRISPR) associated protein (CRISPR/Cas) modalities into this system, we developed SVNs capable of inserting complex DNAs into genomes, at base pair precision. We anticipate a major role for SVNs as an attractive alternative to viral vectors in accelerating genome engineering and gene therapy applications in the future.
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Teekaraman, Yuvaraja, Irina Kirpichnikova, Hariprasath Manoharan, Ramya Kuppusamy, Ravi V. Angadi, and Amruth Ramesh Thelkar. "Diminution of Smart Grid with Renewable Sources Using Support Vector Machines for Identification of Regression Losses in Large-Scale Systems." Wireless Communications and Mobile Computing 2022 (August 8, 2022): 1–11. http://dx.doi.org/10.1155/2022/6942029.
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This article examines the effect of smart grid systems by implementing artificial intelligence (AI) technique with application of renewable energy sources (RES). The current state generation smart grid system follows a high demand on supply of equal energy load to all grid states. However, in conventional techniques, high demand is observed as manual operation is preformed and load problems are not solved within the stipulated time period due to lack of technological advancements. However, applications of AI in smart grid process reduces risk of operation as manual adjustments are converted to highly automated procedures. This type of automatic process identifies the fault location at stage 1 and diagnosis of identified faults will be processed at stage 2. The abovementioned two stage processes will be incorporated with two constant parameters as dummy load is produced to overcome high- to low-power flows. Additionally, a scrap model has been designed to reduce the wastage of power as 100 percent effective progress can be achieved for low- to high-power supplies. To detect the corresponding regression losses in the grid systems, support vector machine (SVM) which completely identifies the previous state loss in the system is integrated. Hence, to analyze the effectiveness of the SVM model, four different scenarios are evaluated and compared with heuristic algorithms, long short-term memory (LSTM), autoregressive indicated moving average (ARIMA), adaptive ARIMA, and linear regression models with distinct performance analysis that includes error in percentage values where a total efficiency of 81% is achieved for projected SVM in all power lines including large-scale systems as compared to existing approaches.
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Molaro, Margherita, Romeel Davé, Sultan Hassan, Mario G. Santos, and Kristian Finlator. "Artist: fast radiative transfer for large-scale simulations of the epoch of reionization." Monthly Notices of the Royal Astronomical Society 489, no. 4 (August 14, 2019): 5594–611. http://dx.doi.org/10.1093/mnras/stz2171.
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ABSTRACT We introduce the ‘Asymmetric Radiative Transfer In Shells Technique’ (artist), a new method for photon propagation on large scales that explicitly conserves photons, propagates photons at the speed of light, approximately accounts for photon directionality, and closely reproduces results of more detailed radiative transfer (RT) methods. Crucially, it is computationally fast enough to evolve the large cosmological volumes required to predict the 21cm power spectrum on scales that will be probed by future experiments targeting the epoch of reionization (EoR). Most seminumerical models aimed at predicting the EoR 21cm signal on these scales use an excursion set formalism (ESF) to model the gas ionization, which achieves computational viability by making a number of approximations. While artist is still roughly two orders of magnitude slower than ESF, it does allow to model the EoR without the need for such approximations. This is particularly important when considering a wide range of reionization scenarios for which artist would help limit the assumptions made. By implementing our RT method within the seminumerical code simfast21, we show that Artist predicts a significantly different evolution for the EoR ionization field compared to the code’s native ESF. In particular, artist predicts up to a factor of two difference in the power spectra, depending on the physical parameters assumed. Its application to large-scale EoR simulations will therefore allow more physically motivated constraints to be obtained for key EoR parameters. In particular, it will remove the need for the artificial rescaling of the escape fraction.
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Trinh, Nguyen, Anh Le Thi Kim, Hung Nguyen, and Linh Tran. "Algorithmic TCAM on FPGA with data collision approach." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 1 (April 1, 2021): 89. http://dx.doi.org/10.11591/ijeecs.v22.i1.pp89-96.
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<span>Content addressable memory (CAM) and ternary content addressable memory (TCAM) are specialized high-speed memories for data searching. CAM and TCAM have many applications in network routing, packet forwarding and Internet data centers. These types of memories have drawbacks on power dissipation and area. As field-programmable gate array (FPGA) is recently being used for network acceleration applications, the demand to integrate TCAM and CAM on FPGA is increasing. Because most FPGAs do not support native TCAM and CAM hardware, methods of implementing algorithmic TCAM using FPGA resources have been proposed through recent years. Algorithmic TCAM on FPGA have the advantages of FPGAs low power consumption and high intergration scalability. This paper proposes a scaleable algorithmic TCAM design on FPGA. The design uses memory blocks to negate power dissipation issue and data collision to save area. The paper also presents a design of a 256 x 104-bit algorithmic TCAM on Intel FPGA Cyclone V, evaluates the performance and application ability of the design on large scale and in future developments.</span>
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LUKE, EDWARD A., and THOMAS GEORGE. "Loci: a rule-based framework for parallel multi-disciplinary simulation synthesis." Journal of Functional Programming 15, no. 3 (May 2005): 477–502. http://dx.doi.org/10.1017/s0956796805005514.
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We present a rule-based framework for the development of scalable parallel high performance simulations for a broad class of scientific applications (with particular emphasis on continuum mechanics). We take a pragmatic approach to our programming abstractions by implementing structures that are used frequently and have common high performance implementations on distributed memory architectures. The resulting framework borrows heavily from rule-based systems for relational database models, however limiting the scope to those parts that have obvious high performance implementation. Using our approach, we demonstrate predictable performance behavior and efficient utilization of large scale distributed memory architectures on problems of significant complexity involving multiple disciplines.
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Li, Yiren, Tieke Li, Pei Shen, Liang Hao, Wenjing Liu, Shuai Wang, Yufei Song, and Liang Bao. "Sim-DRS: a similarity-based dynamic resource scheduling algorithm for microservice-based web systems." PeerJ Computer Science 7 (December 17, 2021): e824. http://dx.doi.org/10.7717/peerj-cs.824.
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Microservice-based Web Systems (MWS), which provide a fundamental infrastructure for constructing large-scale cloud-based Web applications, are designed as a set of independent, small and modular microservices implementing individual tasks and communicating with messages. This microservice-based architecture offers great application scalability, but meanwhile incurs complex and reactive autoscaling actions that are performed dynamically and periodically based on current workloads. However, this problem has thus far remained largely unexplored. In this paper, we formulate a problem of Dynamic Resource Scheduling for Microservice-based Web Systems (DRS-MWS) and propose a similarity-based heuristic scheduling algorithm that aims to quickly find viable scheduling schemes by utilizing solutions to similar problems. The performance superiority of the proposed scheduling solution in comparison with three state-of-the-art algorithms is illustrated by experimental results generated through a well-known microservice benchmark on disparate computing nodes in public clouds.
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Bathla, Gourav, Himanshu Aggarwal, and Rinkle Rani. "Migrating From Data Mining to Big Data Mining." International Journal of Engineering & Technology 7, no. 3.4 (June 25, 2018): 13. http://dx.doi.org/10.14419/ijet.v7i3.4.14667.
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Data mining is one of the most researched fields in computer science. Several researches have been carried out to extract and analyse important information from raw data. Traditional data mining algorithms like classification, clustering and statistical analysis can process small scale of data with great efficiency and accuracy. Social networking interactions, business transactions and other communications result in Big data. It is large scale of data which is not in competency for traditional data mining techniques. It is observed that traditional data mining algorithms are not capable for storage and processing of large scale of data. If some algorithms are capable, then response time is very high. Big data have hidden information, if that is analysed in intelligent manner can be highly beneficial for business organizations. In this paper, we have analysed the advancement from traditional data mining algorithms to Big data mining algorithms. Applications of traditional data mining algorithms can be straight forward incorporated in Big data mining algorithm. Several studies have analysed traditional data mining with Big data mining, but very few have analysed most important algortihsm within one research work, which is the core motive of our paper. Readers can easily observe the difference between these algorthithms with pros and cons. Mathemtics concepts are applied in data mining algorithms. Means and Euclidean distance calculation in Kmeans, Vectors application and margin in SVM and Bayes therorem, conditional probability in Naïve Bayes algorithm are real examples. Classification and clustering are the most important applications of data mining. In this paper, Kmeans, SVM and Naïve Bayes algorithms are analysed in detail to observe the accuracy and response time both on concept and empirical perspective. Hadoop, Mapreduce etc. Big data technologies are used for implementing Big data mining algorithms. Performace evaluation metrics like speedup, scaleup and response time are used to compare traditional mining with Big data mining.
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Commer, Michael, Gregory A. Newman, Kenneth H. Williams, and Susan S. Hubbard. "3D induced-polarization data inversion for complex resistivity." GEOPHYSICS 76, no. 3 (May 2011): F157—F171. http://dx.doi.org/10.1190/1.3560156.
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The conductive and capacitive material properties of the subsurface can be quantified through the frequency-dependent complex resistivity. However, the routine three-dimensional (3D) interpretation of voluminous induced polarization (IP) data sets still poses a challenge due to large computational demands and solution nonuniqueness. We have developed a flexible methodology for 3D (spectral) IP data inversion. Our inversion algorithm is adapted from a frequency-domain electromagnetic (EM) inversion method primarily developed for large-scale hydrocarbon and geothermal energy exploration purposes. The method has proven to be efficient by implementing the nonlinear conjugate gradient method with hierarchical parallelism and by using an optimal finite-difference forward modeling mesh design scheme. The method allows for a large range of survey scales, providing a tool for both exploration and environmental applications. We experimented with an image focusing technique to improve the poor depth resolution of surface data sets with small survey spreads. The algorithm’s underlying forward modeling operator properly accounts for EM coupling effects; thus, traditionally used EM coupling correction procedures are not needed. The methodology was applied to both synthetic and field data. We tested the benefit of directly inverting EM coupling contaminated data using a synthetic large-scale exploration data set. Afterward, we further tested the monitoring capability of our method by inverting time-lapse data from an environmental remediation experiment near Rifle, Colorado. Similar trends observed in both our solution and another 2D inversion were in accordance with previous findings about the IP effects due to subsurface microbial activity.
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Nelson, T., B. Boots, M. A. Wulder, T. Shore, L. Safranyik, and T. Ebata. "Rating the susceptibility of forests to mountain pine beetle infestations: the impact of data." Canadian Journal of Forest Research 36, no. 11 (November 1, 2006): 2815–25. http://dx.doi.org/10.1139/x06-163.
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British Columbia is currently experiencing the largest mountain pine beetle (Dendroctonus ponderosae Hopkins) epidemic on record. The spatial extent of this infestation highlights the need for large-area forest management. We explore the use of three large-area data sets for implementing a stand-scale model of forest susceptibility that quantifies the probability of loss of pine basal area because of attack by the mountain pine beetle. Using these data sets, we investigate the impact of surrogate variables, which is necessary when variables required for the susceptibility model are not present in a data set. The impact of the source data information content on the susceptibility model output is also analyzed. Results indicate that the susceptibility model is sensitive to both surrogate variables and data sources and suggest that landscape level application of the susceptibility model, which was developed using stand-scale relationships, is problematic. Of particular concern is the use of photointerpreted data sets for model parameterization. The information content in photointerpreted data sets is much different than data on similar forest characteristics collected in the field and provides an inadequate substitute for implementing the forest susceptibility model.
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Malik, Abrar, Mir Irfan Ul Haq, Ankush Raina, and Kapil Gupta. "3D printing towards implementing Industry 4.0: sustainability aspects, barriers and challenges." Industrial Robot: the international journal of robotics research and application 49, no. 3 (January 18, 2022): 491–511. http://dx.doi.org/10.1108/ir-10-2021-0247.
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Purpose Environmental degradation has emerged as one of the major limitations of industrial revolution and has led to an increased focus towards developing sustainable strategies and techniques. This paper aims to highlight the sustainability aspects of three-dimensional (3D) printing technology that helps towards a better implementation of Industry 4.0. It also aims to provide a brief picture of relationships between 3D printing, Industry 4.0 and sustainability. The major goal is to facilitate the researchers, scholars, engineers and recommend further research, development and innovations in the field. Design/methodology/approach The various enabling factors for implementation of Industry 4.0 are discussed in detail. Some barriers to incorporation of 3D Printing, its applications areas and global market scenario are also discussed. A through literature review has been done to study the detailed relationships between 3D printing, Industry 4.0 and sustainability. Findings The technological benefits of 3D printing are many such as weight savings, waste minimization and energy savings. Further, the production of new 3D printable materials with improved features helps in reducing the wastage of material during the process. 3D printing if used at a large scale would help industries to implement the concept of Industry 4.0. Originality/value This paper focuses on discussing technological revolution under Industry 4.0 and incorporates 3D printing-type technologies that largely change the product manufacturing scenario. The interrelationships between 3D printing, Industry 4.0 and sustainability have been discussed.
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Yurgaytis, Alexey, Dmitriy Topchiy, and Vitaly Chernigov. "Conducting tensometric monitoring of the technical and stress-strain state of underground facilities." MATEC Web of Conferences 265 (2019): 05036. http://dx.doi.org/10.1051/matecconf/201926505036.
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The authors describe the experience of carrying out a scientific and technical research study as part of scientific and technical support at the construction and design stages of large-scale construction projects in the city of Moscow (including unique ones). Analytical data are presented as a result of studying the stressed-deformed state of a pile foundation with step-by-step application of load in the course of implementing civil construction projects.
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Ma, W., Y. Zhao, P. Zhang, S. Dong, F. Luo, C. Liu, and X. Wu. "RESEARCH ON QUALITY SUPERVISION AND SPOT CHECKS TECHNOLOGY FOR NATIONWIDE LARGE-SCALE TOPOGRAPHIC MAPS IN CHINA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W2 (September 12, 2019): 49–52. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w2-49-2019.
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<p><strong>Abstract.</strong> Large-scale Topographic Maps (LST-Maps) play an important role in China, so their quality is of great importance. In order to guarantee the quality of the survey and mapping achievements, China has been implementing a supervision and inspection system. There are relatively big differences in the content, mode of production and demands for application of the LST-Maps all over the country, making it difficult for current informatization level of supervision and traditional methods of quality control to become fair and efficient. The existing operational mode, sampling means, and technical standards of inspection applied in supervision are not entirely suitable for the needs in quality supervision and inspection of LST-Maps. In face of the abovementioned contradictions and problems, the research has combined the requirements of quality supervision and spot checks of all levels. Various achievements such as an index system, technical means, inspection procedures and a software platform have been created, and they are widely used in the quality supervision and spot checks of national and provincial LST-Maps over the past three years.</p>
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Daulay, Nurussakinah, Wiwin Hendriani, Haerani Nur, and Sayidah Aulia 'ul Haque. "Homeschooling Implementation: Indonesian Parents' Experience During The COVID-19 Pandemic." AL-ISHLAH: Jurnal Pendidikan 13, no. 1 (May 4, 2021): 242–52. http://dx.doi.org/10.35445/alishlah.v13i1.390.
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This study aimed to explore parents' difficulties and efforts in implementing homeschooling during the Covid-19 pandemic from diverse regions in Indonesia. The research was conducted twice, namely three weeks after the enforcement of Large-Scale Social Restriction (PSBB) and ten months after that. The total participants involved were 385 parents in the first study and 105 parents in the second study in this exploratory study. The method used in this study was descriptive qualitative with the data collection using two open-ended questions online. The result showed that schools that had implemented online learning applications before the pandemic would make the parents, teachers, and students easier in facing homeschooling challenges. The school that never implemented online applications before the pandemic made the parents, teachers, and students notice overwhelmed by the challenges of homeschooling. Parents both actively and passively perform how to overcome homeschooling difficulties.
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Ong, Shawn Y., Lisa Stump, Matthew Zawalich, Lisa Edwards, Glynn Stanton, Michael Matthews, and Allen L. Hsiao. "Inpatient Telehealth Tools to Enhance Communication and Decrease Personal Protective Equipment Consumption during Disaster Situations: A Case Study during the COVID-19 Pandemic." Applied Clinical Informatics 11, no. 05 (October 2020): 733–41. http://dx.doi.org/10.1055/s-0040-1719180.
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Abstract Background As the coronavirus disease 2019 pandemic exerts unprecedented stress on hospitals, health care systems have quickly deployed innovative technology solutions to decrease personal protective equipment (PPE) use and augment patient care capabilities. Telehealth technology use is established in the ambulatory setting, but not yet widely deployed at scale for inpatient care. Objectives This article presents and describes our experience with evaluating and implementing inpatient telehealth technologies in a large health care system with the goals of reducing use of PPE while enhancing communication for health care workers and patients. Methods We discovered use cases for inpatient telehealth revealed as a result of an immense patient surge requiring large volumes of PPE. In response, we assessed various consumer products to address the use cases for our health system. Results We identified 13 use cases and eight device options. During device setup and implementation, challenges and solutions were identified in five areas: security/privacy, device availability and setup, device functionality, physical setup, and workflow and device usage. This enabled deployment of more than 1,800 devices for inpatient telehealth across seven hospitals with positive feedback from health care staff. Conclusion Large-scale setup and distribution of consumer devices is feasible for inpatient telehealth use cases. Our experience highlights operational barriers and potential solutions for health systems looking to preserve PPE and enhance vital communication.
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Mohana Krishnan, S., Saurav Rawat, M. Surender, R. Balakrishna, and R. Anandan. "Implementing an Energy Calculator in a Mobile Based Application for Solar Potential Measurement." International Journal of Engineering & Technology 7, no. 3.6 (July 4, 2018): 403. http://dx.doi.org/10.14419/ijet.v7i3.6.16012.
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Solar photovoltaic (PV) technology has matured to become a technically viable large scale source of sustainable energy. Understanding the rooftop PV potential is critical for utility planning, accommodating grid capacity, deploying financing schemes and formulating future adaptive energy policies. The NIWE (National Institute of Wind Energy) under MNRE (Ministry of New and Renewable Energy) is an esteemed institute dedicated to Indian wind and solar renewable energy generation and monitoring. The SRRA (Solar Radiation and Resource Assessment) is a division under NIWE that is responsible for solar energy monitoring throughout India. They have created the Solar Radiation Map of India using high quality, ground measured solar data. This asks the question, whether it is possible to get a quick estimate of a solar installation. Thus, the paper explains the problems in the field of solar potential measurement and the deployment of a calculator in a mobile front platform. The mobile app would quickly and effortlessly give a rough estimate on what a solar installation could save in power consumption costs.
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Omotosho, Adebayo, Peace Ayegba, Justice Emuoyibofarhe, and Christoph Meinel. "Current State of ICT in Healthcare Delivery in Developing Countries." International Journal of Online and Biomedical Engineering (iJOE) 15, no. 08 (May 14, 2019): 91. http://dx.doi.org/10.3991/ijoe.v15i08.10294.
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Electronic health is one of the most popular applications of information and communication technologies and it has contributed immensely to health delivery through the provision of quality health service and ubiquitous access at a lower cost. Even though this mode of health service is increasingly becoming known or used in developing nations, these countries are faced with a myriad of challenges when implementing and deploying e-health services on both small and large scale. It is estimated that the Africa population alone carries the highest percentage of the world’s global diseases despite its certain level of e-health adoption. This paper aims at analyzing the progress so far and the current state of e-health in developing countries particularly Africa and propose a framework for further improvement.
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Hariani, Nova, Wafif Azizah, An Nissa Falaq Qurrahmah, Nur Ulmi, Olivia Yolanda Lawono, Rizka Shofiyya Ramadhani, and Imam Rosadi. "Tingkat Ketaatan Masyarakat dalam Melaksanakan Protokol Kesehatan Selama Pandemi Covid-19 di Kota Samarinda Kalimantan Timur." Infokes: Jurnal Ilmiah Rekam Medis dan Informatika Kesehatan 12, no. 2 (September 26, 2022): 1–6. http://dx.doi.org/10.47701/infokes.v12i2.1588.
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Corona virus disease 2019 (COVID-19) is a disease that caused by the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2). This virus attacks the human respiratory tract and accompanied by initial symptoms such as fever, cough, sore throat, even losing of sense, taste or smell. COVID-19 is still a pandemic in various countries including Indonesia so far. The Indonesian government has implemented various efforts to prevent the transmission of the virus, starting with the implementation of health protocols to maintain personal and environmental hygiene with Large-Scale Social Restrictions since the first positive cases of COVID-19 in 2020. The Samarinda City Government participated in implementing health protocols policies cover Testing, Tracing, and Treatment (3T), Large Scale Social Restrictions, and higher level of social restrictions . This study will research the level of community compliance in implementing Health protocols during the COVID-19 pandemic in Samarinda. This research was conducted through a digital survey using Googleform. The results show that Samarinda citizens has an increase on the application of health protocols as evidenced by the awareness of washing hands and using Hand sanitizer. However, in maintaining distance and self isolation, the community still does not have high awareness enough that has a potential to create new cluster by the opportunities of virus tranmission.