76 research outputs found
Application of the Sahebi model using ALOS/PALSAR and 66.3 cm long surface profile data
No full textSoil moisture is important information for agricultural fields in which erosion of upper soil layers depends upon the soil moisture, and in which the yield depends on soil water content during sowing, growing and harvest periods. Although sensitivity of microwave backscatters to soil moisture is well understood, several factors, such as surface roughness and incidence angle, can interfere with the estimation of soil moisture using Synthetic Aperture Radar (SAR) data. In this letter, we evaluate the influence of these variables using Advanced Land Observing Satellite (ALOS)/Phased Array type L-Band Synthetic Aperture Radar (PALSAR) data and 66.3 cm long surface profile data using the Sahebi model (Sahebi et al. 2003, Estimation of the moisture content of bare soil from RADASAT-1 SAR using simple empirical models. International Journal of Remote Sensing, 24, pp. 2575–2583). The model applied in this study has a root mean square error (RMSE) of only 1.34 dB, which suggests that 66.3 cm long surface profile data are effective for characterization of surface roughness effects on backscattering coefficients
Molecular cloning and functional studies of silicon-responsive serine-rich protein transcripts from mangrove plant, rhizophora apiculata (Blume)
No full textSilicon (Si) is one of the most plentiful elements found in the soil. Silicon plays an important role in decreasing susceptibility of plants against variety of different biotic and abiotic stresses. Mangrove plant (Rhizophora
apiculata) is able to accumulate, and process Si to generate biosilica. Therefore, it would be a beneficial source for genetic manipulation of susceptible plants in the stress conditions. The objectives of the study were (i) to identify and characterize of a Si responsive gene in mangrove,(ii) to analyze the expression levels of a gene encoding serine-rich protein, and (iii) Functional studies of serine-rich protein in Arabidopsis thaliana. Three different methods and RNeasy plant mini kit were used to extract nucleic acids. The Suppression Subtractive Hybridization (SSH) technique was used to remove transcripts from proteins which were not involved in
Si accumulation. Specific primer was designed to get full-length CDS of serine-rich protein. Semi-quantitative RT-PCR and real-time PCR were performed to examine its expression level under the control and treatment conditions. The Gateway Technology was used to construct entry and the
expression vectors. Transformation of Arabidopsis thaliana with serine-rich protein gene was performed using Agrobacterium-mediated transformation by the floral-dip method. Energy-dispersive X-ray spectroscopy and high
performance liquid chromatography were used to measure the quantity of Si and serine amino acid, respectively. Modified CTAB and SDS were quick and reliable methods for isolation of total RNA from the roots and leaves of mangrove, respectively. Of the sequences obtained from cDNA
library, four were 97% similar to serine-rich protein gene of groundnut(Arachis hypogaea). Full-length of the serine-rich protein cDNA obtained through amplification of the cDNA template using specific primers. The expression levels of serine-rich protein transcript were generally higher in
the Si treated mangrove plants than untreated plants. The amount of serine amino acid of transgenic Arabidopsis has increased significantly from 1.02 mg g-1 in wild-type plants to 37.76 mg g-1. In addition, concentration of Si in the leaves and roots of transgenic plant was significantly higher than that in the wild type (P<0.01). This study
successfully determined the Si responsive transcript related to serine-rich protein in mangrove plant (R. apiculata)
Distributed large-scale graph processing on FPGAs
No full textAbstract Processing large-scale graphs is challenging due to the nature of the computation that causes irregular memory access patterns. Managing such irregular accesses may cause significant performance degradation on both CPUs and GPUs. Thus, recent research trends propose graph processing acceleration with Field-Programmable Gate Arrays (FPGA). FPGAs are programmable hardware devices that can be fully customised to perform specific tasks in a highly parallel and efficient manner. However, FPGAs have a limited amount of on-chip memory that cannot fit the entire graph. Due to the limited device memory size, data needs to be repeatedly transferred to and from the FPGA on-chip memory, which makes data transfer time dominate over the computation time. A possible way to overcome the FPGA accelerators’ resource limitation is to engage a multi-FPGA distributed architecture and use an efficient partitioning scheme. Such a scheme aims to increase data locality and minimise communication between different partitions. This work proposes an FPGA processing engine that overlaps, hides and customises all data transfers so that the FPGA accelerator is fully utilised. This engine is integrated into a framework for using FPGA clusters and is able to use an offline partitioning method to facilitate the distribution of large-scale graphs. The proposed framework uses Hadoop at a higher level to map a graph to the underlying hardware platform. The higher layer of computation is responsible for gathering the blocks of data that have been pre-processed and stored on the host’s file system and distribute to a lower layer of computation made of FPGAs. We show how graph partitioning combined with an FPGA architecture will lead to high performance, even when the graph has Millions of vertices and Billions of edges. In the case of the PageRank algorithm, widely used for ranking the importance of nodes in a graph, compared to state-of-the-art CPU and GPU solutions, our implementation is the fastest, achieving a speedup of 13 compared to 8 and 3 respectively. Moreover, in the case of the large-scale graphs, the GPU solution fails due to memory limitations while the CPU solution achieves a speedup of 12 compared to the 26x achieved by our FPGA solution. Other state-of-the-art FPGA solutions are 28 times slower than our proposed solution. When the size of a graph limits the performance of a single FPGA device, our performance model shows that using multi-FPGAs in a distributed system can further improve the performance by about 12x. This highlights our implementation efficiency for large datasets not fitting in the on-chip memory of a hardware device
Application of silicon in plant tissue culture
No full textSilicon (Si) is one of the most plentiful mineral elements in soil. It is a macroelement involved in the responses of plants to a variety of abiotic stresses. The culture medium composition, particularly the mineral nutrients, greatly impacts the growth as well as the morphogenesis of in vitro plant cultures. Numerous morphological and physiological disorders including hyperhydricity, upwardly curled leaves, shoot tip necrosis, and fasciation are often related to inorganic nutrient imbalances of the tissue culture medium. Silicon has been reported to improve many growth parameters including embryogenesis and organogenesis, as well as leaf morphology, physiology, and anatomy. Silicon decreases the susceptibility of plants to salinity and low temperature, alleviates metal toxicity, lessens the incidence of hyperhydricity, and avoids oxidative phenolic browning in various plants. Overall, the evidence indicates a positive role for Si in improving various aspects of plant tissue culture, including micro-propagation, organogenesis, cryopreservation, somatic embryogenesis, and secondary metabolite production
Strategic supplier selection for renewable energy supply chain under green capabilities (fuzzy BWM-WASPAS-COPRAS approach)
No full textThe supplier selection problem (SSP) is a significant issue in renewable supply chain management (RSCM). Selecting a strategic green supplier can not only discover the sustainable development of supply chains but also optimize the consumption rate of resources and decrease the negative environmental effects, which adopts to the green development context. As a multiple criteria group decision-making (MCGDM) problem, choosing a strategic green supplier is important to renewable supply chains. However, how to choose a strategic green supplier for supply chains is a great effort. Hence, In the present work, evaluating a set of strategic suppliers is primarily based on green capabilities by using an integrated fuzzy Best Worst Method (FBWM) with the other two techniques, namely COPRAS (Complex Proportional Assessment of Alternatives) and WASPAS (Weighted Aggregated Sum-Product Assessment). Initially, nine strategic supplier selection criteria have been identified through literature review and a real-world case study of Iran's renewable energy supply chain is deliberated to exhibit the proposed framework's applicability. The applied methodology and its analysis will provide insight to decision-makers of strategic supplier selection. It may aid decision-makers and the procurement department in differentiating the significant strategic green supplier selection criteria and assess the strategic green supplier in the local and global market supply chain. Finally, the strengths and limitation of the framework are discussed by using comparative analysis with other methods.CC BY 4.0Corresponding author: Masood Fathi, Division of Intelligent Production Systems, School of Engineering Science, University of Skövde, 54128, Skövde, Sweden. E-mail addresses: [email protected] (B. Masoomi), [email protected] (I.G. Sahebi), [email protected] (M. Fathi), [email protected] (F. Yıldırım), [email protected] (S. Ghorbani).</p
Accelerating Large-Scale Graph Processing with FPGAs: Lesson Learned and Future Directions
No full textProcessing graphs on a large scale presents a range of difficulties, including irregular memory access patterns, device memory limitations, and the need for effective partitioning in distributed systems, all of which can lead to performance problems on traditional architectures such as CPUs and GPUs. To address these challenges, recent research emphasizes the use of Field-Programmable Gate Arrays (FPGAs) within distributed frameworks, harnessing the power of FPGAs in a distributed environment for accelerated graph processing. This paper examines the effectiveness of a multi-FPGA distributed architecture in combination with a partitioning system to improve data locality and reduce inter-partition communication. Utilizing Hadoop at a higher level, the framework maps the graph to the hardware, efficiently distributing pre-processed data to FPGAs. The FPGA processing engine, integrated into a cluster framework, optimizes data transfers, using offline partitioning for large-scale graph distribution. A first evaluation of the framework is based on the popular PageRank algorithm, which assigns a value to each node in a graph based on its importance. In the realm of large-scale graphs, the single FPGA solution outperformed the GPU solution that were restricted by memory capacity and surpassing CPU speedup by 26x compared to 12x. Moreover, when a single FPGA device was limited due to the size of the graph, our performance model showed that a distributed system with multiple FPGAs could increase performance by around 12x. This highlights the effectiveness of our solution for handling large datasets that surpass on-chip memory restrictions.Quantum Circuit Architectures and Technolog
Correlates of self-reported driving aberrations in Tehran: A study at the level of drivers and districts
No full textThere are relatively few comprehensive studies on driving errors and violations in Iran, a non-Western country with a high traffic fatality rate. In this study, 712 drivers completed a questionnaire at technical inspection centres and carwashes in Tehran, Iran. Respondents were asked about their demographic characteristics, accident involvement, traffic fines, and driving aberrations in the form of the Driver Behaviour Questionnaire (DBQ). The results of a principal component analysis of the DBQ showed a distinction between errors and two types of violations: speeding and non-speeding violations. Correlation analyses showed that DBQ violations were associated with a higher driving mileage, a higher education level (for DBQ speeding violations in particular), and younger age. DBQ errors were associated with risk perception, that is, the belief that one has a high probability of becoming involved in a car accident. Regression analyses showed that the DBQ speeding violations score was predictive of the number of speeding tickets and that the DBQ non-speeding violations score was predictive of involvement in minor accidents in the past three years. A correlation analysis at the level of municipal districts showed that drivers from districts with lower education and literacy levels and lower car ownership were more likely to report driving a low-cost car and had lower DBQ violations scores. These results can be interpreted as indicating that affluence enables deviant driving. We conclude that the error-violation distinction is of relevance to road safety in Tehran, both at the level of individual drivers and at the level of districts.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Human-Robot Interactio
An in silico approach for characterization of encoded protein from Rdr1, a black spot resistance gene in Rosa multiflora
No full textIncorporating car owner preferences for the introduction of economic incentives for speed limit enforcement
No full textHuman error including driving misbehavior contributes to over 90 percent of road vehicle accidents, and speeding is considered to be risky. Smart technologies, such as Connected Vehicle System (CVS) are among the interesting technical options to improve driving behavior, and Pay-As-You-Speed (PAYS) is an effective economic incentive to reduce speed violations. We investigated the acceptability of CVS with and without the presence of economic incentives, such as PAYS, in the context of a middle-income country: Iran. We used a Zero-Inflated Ordered Probit model (ZIOP) to estimate drivers’ willingness to pay for a CVS, and a hazard-based model for predicting the incentive level needed for accepting CVS via a PAYS scheme. ZIOP model indicated that drivers with the following characteristics were more likely to pay more for CVS: having a comprehensive insurance coverage, being younger than 60 years, owning more than one car, and having older vehicles. The hazard-based model also confirmed that drivers that speed relatively often have a lower tendency to adopt CVS, and drivers who experienced an accident in the past were more inclined to adopt CVS via PAYS. Also, drivers' opinion about CVS, vehicle characteristics, demographics, and driving experience influenced the effect of PAYS characteristics on acceptability of CVS. Finally, we offer recommendations for how to effectively implement CVS, in order to significantly reduce the high fatality and accident rates in middle-income countries such as Iran.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport and Logistic
Customer Needs Linked to Production Strategy and Firm’s Dynamic Capabilities
No full textThis study identifies and analyzes the key factors of an efficient customer oriented production strategy. The paper supports the view that dynamic capabilities can be used successfully for improving company’s efficiency. This research is a case study research related to production strategy. In a case company, totally 31 interviews were made from 16 subsidiaries and 13 from end customers. The present paper focuses more on end customers’ than internal customers.Key findings can be identified as follows: effective information flow, flexibility, speed and responsiveness need more focus in the study of dynamic capabilities. Delivery accuracy is the key, while short delivery time is a competitive factor. This research is focused to power electronics business segment which is research limitation. To make wider conclusions, more empirical studies are needed. As a Practical implications,this research helps firms to improve their strategy process by understanding real customer needs. The research results bring additional value to the previous studies regarding company strategy, business environment, innovativeness and operational excellence.Copyright © 2013 Polish Association for Production Management. Under the Creative Commons Attribution-NonCommercial-NoDerivs license, the author(s) and users are free to share (copy, distribute and transmit the contribution) under the following conditions: 1. they must attribute the contribution in the manner specified by the author or licensor, 2. they may not use this contribution for commercial purposes, 3. they may not alter, transform, or build upon this work.fi=vertaisarvioitu|en=peerReviewed
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