76,369 research outputs found
In-Memory Database Query Energy Estimation: Modeling & Green Strategy Support
No full textThe miniaturization of electronic components, coupled with falling acquisition prices and increasing capacities, has led to the availability of many types of main memory data storage systems called In-Memory databases. Despite their inability to handle large volumes of data in memory, these systems offer considerable performance in query processing. This is due to the latency optimization of loading data from secondary memory. Nevertheless, their operation requires an execrable use of the main memory and also high energy consumption. With the torch of environmental sustainability being waved and the exorbitant energy cost, the development and application of energy reduction techniques within these systems is more urgent than ever. In this paper, we model the cost of energy consumption during query plan execution in an In-Memory database to develop energy-efficient approaches for query processing and benchmark tool designing
Enhancing Agricultural Practices Through Iot And Decision Tree Analytics: A Case Study On Autonomous Irrigation Management
No full textAgriculture, essential for meeting human needs, faces complex challenges related to variable environmental conditions, overuse of resources, and inefficient distribution processes. Precision agriculture emerges as an optimal solution by utilizing advanced technologies like the Internet of Things (IoT) to collect detailed data on agricultural conditions. With artificial intelligence-based techniques, it is possible to optimize production and reduce environmental impact. This study explores the use of technological paradigms such as the Internet of Things and Artificial Intelligence in agriculture, focusing on integrating decision trees for analysis and management. This approach could enable more effective management of agricultural variables such as temperature, humidity, and soil nutrients, providing farmers with a solid foundation for making informed decisions. Preliminary results demonstrate that integrating decision trees in precision agriculture can significantly improve the effectiveness and sustainability of the sector, providing valuable guidance for future smart agricultural practices
Digital Twin for Predictive Monitoring of Crops: State of the Art
No full textRecently, the use of digital twins in crop management has caught the attention of the agricultural sector. This technology is still in its early phases of deployment, and the state-of-the-art methodologies and adoption level of digital twins have not been thoroughly explored. To address this issue, this paper discusses the current trend of crop predictive monitoring using digital twin applications, focusing on the approaches used, adoption levels, and implementation challenges. Digital twins in crop management are still in the lab stage, and large-scale implementations in farming are not reported. Despite the benefits of increased crop productivity, the adoption of digital twins is hampered by challenges such as the complexity of modeling, poor high-speed Internet connectivity in rural areas, data security, significant investment costs, data accuracy, and a lack of knowledge about crop types and farming circumstances. Insights are provided to research academics, companies, and practitioners to help them understand the current state-of-the-art problems and future research prospects in the sector
A Comprehensive Energy Modeling Approach for Query Processing: Steps and Machine Learning Influence
No full textPortrait of Amy Mack (Mrs Lancelot Harrison) [picture] /
No full textTitle from inscription on reverse.; Condition: Fair, glued to card.; Inscriptions: "Amy Mack (Mrs. Lancelot Harrison) author of 'A bush calendar', 'Bush days', etc. photo. J. S. P. Ramsay" --In ink on reverse
Profiling of Soluble Neutral Oligosaccharides from Treated Biomass using Solid Phase Extraction and Liquid Chromatography-Multiplexed Collision Induced Dissociation-Mass Spectrometry
No full textThermochemical pretreatment of cellulosic biomass improves cell wall enzymatic digestibility, while simultaneously releasing substantial amounts of soluble oligosaccharides. Profiling of oligosaccharides released during pretreatment yield information essential for choosing glycosyl hydrolases necessary for cost-effective conversion of cellulosic biomass to desired biofuel/biochemical end-products. In this report we present a methodology for profiling of soluble neutral oligosaccharides released from ammonia fiber expansion (AFEXTM)-pretreated corn stover. Our methodology employs solid phase extraction (SPE) enrichment of oligosaccharides based on porous graphitized carbon (PGC), followed by high performance liquid chromatography (HPLC) separation using a polymeric amine based column (Prevail Carbohydrate ES) and electrospray ionization time-of-flight mass spectrometry (ESI-TOF-MS) in both positive and negative modes. For structural elucidation on the chromatographic time scale, nonselective multiplexed collision-induced dissociation was performed for quasi-simultaneous acquisition of accurate molecular and fragment masses of neutral oligosaccharids in a single analysis. These analyses directly revealed presence of glucans up to degree of polymerization (DP) 22 without side-chain modifications. Additionally, arabinoxylans with DP up to 6 were detected in the pretreated biomass samples (post-enzymatic digestion). All linkages between sugar units in glucans and arabinoxylans were identified to be p-1-4 linkages based on cross-ring fragment masses. Comprehensive profiling of soluble oligosaccharides also demonstrated that arabinoxylan acetylation was reduced by greater than 85% post-AFEXTM treatment.Published version: Vismeh, Ramin, Humpula, James F., Chundawat, Shishir P. S., Balan, Venkatesh, Dale, Bruce E. & Jones, A. Daniel. (2013). Profiling of Soluble Neutral Oligosaccharides from Treated Biomass using Solid Phase Extraction and LC-TOF MS. Carbohydrate Polymers 94(2), 791-799. http://dx.doi.org/10.1016/j.carbpol.2013.02.00
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Experimental study of thin film sensor networks for wind turbine blade damage detection
No full textDamage detection of wind turbine blades is difficult due to their complex geometry and large size, for which large deployment of sensing systems is typically not economical. A solution is to develop and deploy dedicated sensor networks fabricated from inexpensive materials and electronics. The authors have recently developed a novel skin-type strain gauge for measuring strain over very large surfaces. The skin, a type of large-area electronics, is constituted from a network of soft elastomeric capacitors. The sensing system is analogous to a biological skin, where local strain can be monitored over a global area. In this paper, we propose the utilization of a dense network of soft elastomeric capacitors to detect, localize, and quantify damage on wind turbine blades. We also leverage mature off the shelf technologies, in particular resistive strain gauges, to augment such dense sensor network with high accuracy data at key locations, therefore constituting a hybrid dense sensor network. The proposed hybrid dense sensor network is installed inside a wind turbine blade 1:25 scale model, and tested in a wind tunnel to simulate an operational environment. Results demonstrate the ability of the hybrid dense sensor network to detect, localize, and quantify damage.</p
Prompt charm production in pp collisions at √<span style="text-decoration:overline">s</span>=7 TeV
No full textCharm production at the LHC in pp collisions at s√=7 TeV is studied with the LHCb detector. The decays D0→K−π+, D+→K−π+π+, D⁎+→D0(K−π+)π+, D+s→ϕ(K−K+)π+, Λ+c→pK−π+, and their charge conjugates are analysed in a data set corresponding to an integrated luminosity of 15 nb−1. Differential cross-sections dσ/dpT are measured for prompt production of the five charmed hadron species in bins of transverse momentum and rapidity in the region 0<pT<8 GeV/c and 2.0<y<4.5. Theoretical predictions are compared to the measured differential cross-sections. The integrated cross-sections of the charm hadrons are computed in the above pT-y range, and their ratios are reported. A combination of the five integrated cross-section measurements gives
σ(cc¯)pT<8 GeV/c,2.0<y<4.5=1419±12(stat)±116(syst)±65(frag) μb,
where the uncertainties are statistical, systematic, and due to the fragmentation functions
- …
