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graphic: Assisted by using Ben-Gurion school of the Negev scientists at the Jacob Blaustein Institutes for barren region analysis, the neighborhood will drudgery to calibrate digital prediction fashions for optimized software of crop... view greater
credit score: Netafim/BGU
BEER-SHEVA, Israel...February 7, 2019 - Bayer, Netafim and BGN applied sciences, the technology switch company of Israel's Ben-Gurion tuition of the Negev (BGU), introduced a 3-year research collaboration. The announcement become made the day past at the Fruit Logistica 2019 exchange impartial and exhibition in Berlin.
The task will blend leading soil analysis, digital prediction paraphernalia and state-of-the paintings drip know-how developed with the aid of Netafim to establish most advantageous practices for the usage of drip irrigation as a birth paraphernalia for Bayer Velum best in Israel. Velum leading combats nematodes (roundworm) and harmful ailments affecting potatoes, tomatoes, cucumbers, citrus, and brassicas.
"This collaboration agreement with industry leaders such as Bayer and Netafim is a testament to BGU's leading position within the container of agriculture," stated Netta Cohen, chief govt officer of BGN technologies. "determined in the coronary heart of the Negev barren region, the analysis at the tuition's Jacob Blaustein Institutes for barren region analysis on the Sede Boqer campus focuses on water, power, agricultural, and ecological options for violent barren region and drylands circumstances, which are increasingly becoming extra significant with environmental alterations comparable to international warming."
as a result of clinical and environmental considerations, employ of chemical crop protection is being confined, leading to loss of inevitable products. Farmers are actually in quest of sustainable agricultural solutions to preserve crop yields.
Bayer and Netafim gain addressed this problem with a brand recent innovative reply named DripByDrip. The thought uses Netafim's drip irrigation techniques to deliver Bayer's chemical and organic crop insurance policy items.
DripByDrip applies the lively materials exactly to the plant, leading to greater efficacy, lessen amounts of crop insurance policy compound and lesser environmental affect.
Assisted by means of BGU scientists from the Blaustein Institutes, the group will drudgery to calibrate digital prediction models for optimized utility of crop insurance contrivance compounds by artery of drip irrigation. This includes laboratory and container experiences evaluating the conduct of Bayer Velum major in soils and flowers below common agricultural conditions in arid regions.
research may breathe performed at the Blaustein Institutes and Netafim facilities in Israel. It may breathe led by using BGU Prof. Shimon Rachmilevitch, Dr. Marc Rist of the Bayer Crop Science Division, and Dubi Raz, corporate agronomy director at Netafim.
Holger Weckwert, portfolio management section supervisor for insecticides solutions at the Bayer Crop Science Division, referred to, "applying crop insurance policy compounds the employ of drip irrigation accurately ambitions the principal vegetation, and is an environmentally friendly, efficient and low-priced contribution to sustainable agriculture. They look forward to taking fragment with leading BGU scientists."
"Drip irrigation gives you water, fertilizers and crop insurance plan, together with biologicals, at once to the roots of the crops," says Netafim's Dubi Raz. "This routine enables farmers to observe crop protection products in a extra targeted routine using Netafim's drip irrigation expertise. Following their a success lengthy-time period partnership with Bayer, we're completely satisfied to now welcome BGU's scientists in helping us in optimizing the software of crop coverage compounds by the employ of drip irrigation."
Bayer is a world commercial enterprise with core abilities in the lifestyles science fields of fitness keeping and agriculture. Its products and functions are designed to capitalize people and better their first-rate of lifestyles. on the identical time, the community goals to create value through innovation, enlarge and inordinate earning power. Bayer is dedicated to the ideas of sustainable construction and to its social and ethical obligations as a company citizen. In fiscal 2017, the neighborhood employed around 99,800 individuals and had income of EUR 35 billion. Capital costs amounted to EUR 2.four billion, R&D fees to EUR four.5 billion. For greater information, proceed to http://www. bayer. com .
About BGN technologies
BGN applied sciences is the expertise switch enterprise of Ben-Gurion college of the Negev in Israel. BGN brings technological improvements from the lab to the market and fosters research collaborations and entrepreneurship amongst researchers and students. so far, BGN has established over 100 startup corporations within the fields of biotech, excessive-tech and cleantech, in addition to initiated leading know-how hubs, incubators and accelerators. over the eventual decade, BGN applied sciences has concentrated on creating long-term partnerships with multinational establishments similar to Deutsche Telekom, Dell-EMC, IBM, PayPal, Bayer, ICL, and Adama, securing value and enlarge for Ben-Gurion college as smartly as the Negev location. For more assistance, talk over with the BGN technologies web page.
Netafim is the global leader in precision irrigation for a sustainable future. With 29 subsidiaries and 17 manufacturing flowers worldwide, Netafim offers innovative, tailor-made irrigation and fertigation solutions to thousands and thousands of farmers, enabling smallholders to massive-scale agricultural producers in over 110 nations to grow extra with much less. headquartered in 1965, Netafim pioneered the drip revolution, making a paradigm shift towards precision irrigation. nowadays, that specialize in end-to-conclusion solutions from the water supply to the basis zone, Netafim promises turnkey irrigation and greenhouse tasks supported by using engineering, task management and financing features. Netafim is besides leading the artery in digital farming, integrating true-time monitoring, evaluation and automatic ply into one state-of-the-artwork system. In 2017, Mexichem, a leading employer of imaginative options in the constructing and infrastructure, facts communications, irrigation and chemical compounds industries acquired eighty p.c of Netafim. Kibbutz Hatzerim holds the final 20 p.c. learn extra at http://www. netafim. com
About American buddies, Ben-Gurion tuition of the Negev
American acquaintances, Ben-Gurion tuition of the Negev (AABGU) plays a essential function in sustaining David Ben-Gurion's imaginative and prescient: growing an international-classification establishment of training and analysis in the Israeli wilderness, nurturing the Negev neighborhood and sharing the school's talents in the neighborhood and world wide. As Ben-Gurion college of the Negev (BGU) appears forward to turning 50 in 2020, AABGU imagines a future that goes past the walls of academia. it is a future the residence BGU invents a brand recent world and inspires a imaginative and prescient for a better Israel and its subsequent generation of leaders. along with supporters, AABGU will assist the school foster excellence in instructing, research and outreach to the communities of the Negev for the subsequent 50 years and past. dispute with imaginative and prescient.aabgu.org to gain scholarship of greater.
AABGU, headquartered in ny, has 9 regional workplaces birthright through the us. For greater tips dispute with https:/ / aabgu. org/ .
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Big-data company Databricks Inc. is topping up its war chest with a hefty $250 million late-stage round of funding.
The sequence E round was led by Andreessen Horowitz. Coatue Management, Microsoft Corp. and recent Enterprise Associates besides participated in the round, which brings Databricks’ total capital raised to date to $498.5 million.
Databricks sells a Unified Analytics Platform based on the open-source Apache Spark big-data framework that’s used by enterprises to analyze data, build data pipelines across siloed storage systems and prepare labeled datasets for model building. The thought is that organizations can then employ the Unified Analytics Platform to train machine learning and other artificial intelligence models using their existing data.
The platform besides provides collaborative features intended to foster closer communication between data scientists and engineers who requisite to drudgery together in order to build better AI models.
Led by the founders of Apache Spark, Databricks has helped to drive massive adoption of that open-source platform in the eventual few years. In its press release, the privately owned company said it managed to top $100 million in annual recurring revenue during 2018 while tripling year-over-year growth in its subscription revenue.
Ali Ghodsi (pictured), Databricks’ cofounder and chief executive officer, said those numbers beget Databricks one of the fastest-growing enterprise software companies around.
“What’s driving this incredible growth is the market’s massive appetite for Unified Analytics,” Ghodsi said in a statement. “Organizations requisite to achieve success with their AI initiatives and this requires a Unified Analytics Platform that bridges the divide between colossal data and machine learning.”
Databricks’ growth has been helped by some useful updates over the past year that aimed to expand the utility of its Unified Analytics Platform. Back in March for example, the company launched a cloud-based offering called Microsoft Azure Databricks that can easily breathe deployed on Microsoft’s Azure cloud and integrated with services such as the Azure SQL Data Warehouse.
Other recent features added over the eventual year comprise Databricks Runtime For ML, which provides preconfigured modeling and distributed training environments. Tightly integrated with such accepted AI frameworks and libraries as TensorFlow, Scikit-Learn, Keras and XGBoost, they serve to accelerate environment provisioning and configuration management.
The company besides introduced Databricks MLflow, which provides an open source toolkit for simplifying ML modeling, training and operationalization across multiclouds. The offering integrates closely with Apache Spark, SciKit-Learn, TensorFlow and other open-source frameworks.
Such progress is positive, but in an article eventual summer, analyst James Kobielus of Wikibon, owned by the identical company as SiliconANGLE, pointed out that Databricks faces significant challenges in trying to sustain its growth in a market where Apache Spark’s adoption is likely to plateau in a similar fashion to how Hadoop’s has. Kobielus notes that Apache Spark is starting to display its age, and feels increasingly enjoy a legacy technology in many AI shops. Moreover, he said, its recent recent offerings don’t signal a pellucid differentiation for Databricks in a market that has become saturated with AI DevOps, modeling and training tools offered by the likes of Amazon Web Services Inc. and Google LLC, among others.
Still, Spark remains the industry’s most dominant runtime engine and library for in-memory, parallel machine learning, as well as a useful appliance in many AI platforms for efficient data preparation and training for many modeling initiatives, Kobielus said. As such, the analyst said, he believes Databricks will breathe able to employ today’s funding to innovate further and overcome the the challenges he highlighted.
“Databricks is able to attract so much funding because the larger data-science workbench market, aka ‘AI DevOps,’ is booming,” Kobielus said. He illustrious that Databricks now offers a “full-featured data science workbench” that’s not only fairly develope in comparison to others, but besides provides a smooth upgrade path for data scientists working on sophisticated AI projects.
Those sentiments were shared by analyst Doug Henschen of Constellation Research Inc., who told SiliconANGLE that he believes Spark will view yet more adoption in the enterprise both for its data-processing and data-transformation capabilities, and for the advanced analytical drudgery it performs.
“Databricks faces competition both from third-party Spark services and emerging analytics platforms and studios such as Amazon SageMaker and Google AI Hub,” Henschen said. “The higher value is in the model-development work, and that’s where lots of alternatives to Databricks are emerging.”
Kobielus besides illustrious the power of Databrick’s rivals’ offerings but said the company remains a pacesetter in the data science market.
“It’s an exceptionally solid brand elope by dynamic visionaries who are doing excellent drudgery and gain loyal customers,” Kobielus said. “My sense is that they’re more likely to prevail going forward in this changed market than the combined Cloudera/Hortonworks is, based on the strengths of their respective machine learning, abysmal learning and artificial intelligence evolution platforms.”
Databricks certainly has a contrivance to Do just that. In a statement to SiliconANGLE, the company said it’s aiming to employ its recent funds to double down on its growth in Asia, Europe and the Middle East, and the Americas. This includes expanding its sales teams in those regions, more investment in engineering, and opening recent offices in several countries across the globe.Photo: SiliconANGLE Since you’re here …
… We’d enjoy to inform you about their mission and how you can abet us fulfill it. SiliconANGLE Media Inc.’s traffic model is based on the intrinsic value of the content, not advertising. Unlike many online publications, they don’t gain a paywall or elope banner advertising, because they want to support their journalism open, without influence or the requisite to chase traffic.The journalism, reporting and commentary on SiliconANGLE — along with live, unscripted video from their Silicon Valley studio and globe-trotting video teams at theCUBE — prefer a lot of arduous work, time and money. Keeping the trait elevated requires the support of sponsors who are aligned with their vision of ad-free journalism content.
If you enjoy the reporting, video interviews and other ad-free content here, please prefer a flash to check out a sample of the video content supported by their sponsors, tweet your support, and support coming back to SiliconANGLE.
Feb 08, 2019 (3BL Media via COMTEX) -- SOURCE:Booz Allen Hamilton
From government spending figures to public health statistics to geospatial imagery, the federal government is gathering, storing, and sharing more data than ever. Agencies gain started consolidating this information into colossal data platforms for analysis and decision-making. But often such a "data lake" threatens to become a "data swamp" that slows agencies' growth and progress.
What's holding agencies back? First of all, it's arduous to combine, drudgery with, and analyze data that comes in various sources, formats, and types--especially given the strict security and privacy regulations federal agencies requisite to follow. People trained in data science are often in short supply and expensive to hire. Moreover, 60 percent of colossal data initiatives overall fail--often because organizations failed to first define the problem they were trying to solve.
In a recent issue of InsideBIGDATA magazine, Booz Allen Senior Associate Chris Brown and Principal David Cunningham provided five tips for moving from "data lakes" to actionable insight.
Read the complete InsideBIGDATA article and learn more about how Booz Allen is using data science and analytics to abet clients solve problems.
Tweet me:How can federal agencies maximize the #BigData they collect? @BoozAllen shares tips with @insideBigData http://bit.ly/2SkvHbd
KEYWORDS: Booz Allen Hamilton, Innovation and Technology, NYSE: BAH, colossal Data, data science, analytics, InsideBIGDATA, data structures
AMES, Iowa – The immense number of feasible hybrids that can breathe created from inbred corn plants can leave plant breeders wondering where to start when attempting to submit recent crop varieties with desirable traits. But recent research from an Iowa state University agronomist shows how advanced data mining techniques can enhance the efficiency of the process.
The study, published recently in the peer-reviewed academic journal Molecular Plant, outlines several data management approaches that can abet plant breeders prognosticate the traits of potential hybrids faster and cheaper than growing and testing the plants. Jianming Yu, professor of agronomy and Pioneer Distinguished Chair in Maize Breeding, said the study shows how genomic selection, or making accurate predictions of how a plant will accomplish based on its genotype, can inform plant breeding decisions.
Recent advances in biotechnology gain allowed scientists to compile massive amounts of genomic data, mapping much of the genetic material present in various organisms, Yu said. But matching the genotype of an organism with its real-world, observable traits, or phenotype, presents a different set of challenges.
The ISU researchers worked with a multi-institution team, including scientists from the University of Missouri, the University of North Carolina and the University of Delaware. The team started the project by generating phenotypic metrics for 276 corn hybrids. The team explored a range of data mining approaches to a dataset containing genomic information as well as phenotypic metrics of these hybrids. Using a suite of data mining methods, they predicted flowering time, ear height and submit for each hybrid, then compared their predictions to the true result and establish their routine was more accurate than previous prediction models.
“With these recent methods, training samples for pile prediction models were more informative so that prediction accuracy was higher,” said Tingting Guo, the first author of the paper and a postdoctoral research associate in agronomy.
The team applied the methods to two other crops, wheat and rice, for which experimental records were available for a large number of hybrids from earlier studies. The approach besides worked well for these other crops.
The research demonstrated that effective genomic prediction models can breathe established with a training set of 2-13 percent of the size of the total set, enabling an efficient exploration of many genetic combinations.
The data mining methods tested in the study drew on several disciplines, including computer science and statistics. The techniques lanky on clustering and vivid networking, but Yu described the approach as a artery of recognizing patterns in vast amounts of data.
“Data mining is another artery of epigram pattern finding,” Yu said. “We looked at a large dataset and establish patterns linking genotype to phenotype. They then reply the question, ‘Which artery of selecting the samples would allow breeders to exploit the patterns with minimal initial efforts?’”
Yu said this line of research could bridge the gap between predicting traits and designing better crops. Optimizing the process of how plant breeders select genetic lines to test will allow for more strategic employ of time and effort, leading to more rapid progress in developing crop varieties with desirable traits.
Funding for the research came from the National Science Foundation and the ISU Plant Sciences Institute. Other ISU scientists contributing to the research comprise Xiaoqing Yu, a former ISU postdoctoral research associate; Xianran Li, an adjunct associate professor of agronomy; and Haozhe Zhang, a graduate lieutenant in statistics; and Chengsong Zhu, a former ISU postdoctoral research associate.
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