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News Article | May 24, 2017
Site: www.marketwired.com

IRVINE, CA--(Marketwired - May 24, 2017) - Signature Devices, Inc. ( : SDVI) today announced that the board of directors has appointed Inas Azzam as CEO and President. Mr. Azzam is a veteran of the technology space and his most recent start-up MorpheusAV was part of the merger with Innovo. Innovo Technology, a leading software and hardware development company focused on the Internet of Things. Mr. Azzam is a solutions-driven and results-based professional with proven IT development and management skills in the US and international markets. He has an entrepreneurial background with strong management skills and an effective ability to deliver targeted solutions backed by over 27 years of technical, management and teaching experiences. Mr. Azzam graduated from UCLA with a degree in Applied Mathematics and Differential Equations, and received his MBA in International Business from SDSU. "I am excited to take on this challenge, and work diligently to drive value for our shareholders," said Mr. Azzam. "Mr. Azzam is the ideal person to lead the company towards the goal of becoming one of the leaders in the development of Internet of Things," said Charles Townsend, Chairman and Interim CEO. "The board felt that Inas has the technical background mixed with years of operations experience to grow the company and build shareholder value," he concluded. You can also follow the company on Twitter for small updates and announcements, https://twitter.com/SignatureDevice Based in Irvine, California, Innovo Technology combines the best of the technologies underpinning the popular Morpheus media server, Tazerwear's AI Software, Truck IT's beacon platform with Knoton's hardware, infrastructure and software. The result is a company that blends custom software and powerful hardware IoT-interconnected devices. Learn more by visiting: http://innovotech.io/about/ Based in Sheridan, Wyoming, Signature Devices, Inc. (www.signaturedevices.com) ( : SDVI) is a holding company with subsidiaries that develop Internet of Things (IOT) products through its subsidiary Innovo Technologies, Inc., and publishes diverse media products including video games and mobile applications through its subsidiary Graffiti Entertainment, Inc. The information in this press release includes certain "forward-looking" statements within the meaning of the Safe Harbor provisions of Federal Securities Laws. Investors are cautioned that such statements are based upon assumptions that in the future may prove not to have been accurate and are subject to significant risks and uncertainties, including the future financial performance of the Company. Although the Company believes that the expectations reflected in its forward-looking statements are reasonable, it can give no assurance that such expectations or any of its forward-looking statements will prove to be correct. Readers are cautioned not to place undue reliance on these forward-looking statements that speak only as of the date of this release, and the Company undertakes no obligation to update publicly any forward-looking statements to reflect new information, events, or circumstances after the date of this release except as required by law.


Researchers have shown how singularities – which are normally only found at the centre of black holes and hidden from view – could exist in highly curved three-dimensional space. CAMBRIDGE, 24-May-2017 — /EuropaWire/ — The researchers, from the University of Cambridge, have used computer simulations to predict the existence of a so-called naked singularity, which interferes with Einstein’s general theory of relativity. This is the first time that a naked singularity, which causes the laws of physics to break down, has been predicted in three-dimensional space. The findings are reported in the journal Physical Review Letters. Einstein’s general theory of relativity underpins our current understanding of gravity: everything from the estimation of the age of the stars in the universe, to the GPS signals we rely on to help us navigate, is based on his equations. In part, the theory tells us that matter warps its surrounding spacetime, and what we call gravity is the effect of that warp. In the 100 years since it was published, general relativity has passed every test that has been thrown at it, but one of its limitations is the existence of singularities. A singularity is a point where gravity is so intense that space, time, and the laws of physics, break down. General relativity predicts that singularities exist at the centre of black holes, and that they are surrounded by an event horizon – the ‘point of no return’, where the gravitational pull becomes so strong that escape is impossible, meaning that they cannot be observed from the outside. For more than 40 years, mathematicians have proposed that whenever singularities form, they will always be hidden from view in this way – this is known as the ‘cosmic censorship conjecture.’ If true, cosmic censorship means that outside of black holes, these singularities have no measurable effect on anything, and the predictions of general relativity remain valid. In recent years, researchers have used computer simulations to predict the existence of ‘naked singularities’ – that is, singularities which exist outside an event horizon. Naked singularities would invalidate the cosmic censorship conjecture and, by extension, general relativity’s ability to explain the universe as a standalone theory. However, all of these predictions have been modelled on universes which exist in higher dimensions. For example, in 2016, two Cambridge PhD students predicted the existence of a naked singularity, but their predictions were based on a five-dimensional universe. The new research, by Toby Crisford and Jorge Santos from Cambridge’s Department of Applied Mathematics and Theoretical Physics, has predicted the existence of a naked singularity in a four-dimensional universe – three spatial dimensions, plus time – for the first time. Their predictions show that a naked singularity can form in a special kind of curved space known as anti-de Sitter space, in which the universe has a distinctive ‘saddle’ shape. According to general relativity, universes can have various shapes, and anti-de Sitter space is one of these possible shapes. Anti-de Sitter space has a very different structure to flat space. In particular it has a boundary which light can reach, at which point it is reflected back. “It’s a bit like having a spacetime in a box,” said Crisford. “At the boundary, the walls of the box, we have the freedom to specify what the various fields are doing, and we use this freedom to add energy to the system and eventually force the formation of a singularity.” While the results are not directly applicable to our universe, as ‘forcing’ a singularity is not a procedure which is possible to simulate in flat space, they do open up new opportunities to study other theories to understand the universe. One such theory could involve quantum gravity, which provides new equations close to a singularity. “The naked singularity we see is likely to disappear if we were to include charged particles in our simulation – this is something we are currently investigating,” said Santos. “If true, it could imply a connection between the cosmic censorship conjecture and the weak gravity conjecture, which says that any consistent theory of quantum gravity must contain sufficiently charged particles. In anti-de Sitter space, the cosmic censorship conjecture might be saved by the weak gravity


News Article | May 25, 2017
Site: www.eurekalert.org

The invasion of nonnative species has widespread and detrimental effects on both local and global ecosystems. These intruders often spread and multiply prolifically, overtake and displace native species, alter the intended interactions between flora and fauna, and damage the environment and economy. A particularly pesky invader is the zebra mussel (Dreissena polymorpha). Given its abundancy, fecundity, and heartiness, zebra mussels frequently outcompete native bivalves. Their dominance interrupts the natural cycle of nutrients and disrupts the structure and function of infested waterworks. These so-called "ecosystem engineers" generate substantial removal costs for individuals, corporations, and towns; estimates indicate that zebra mussels cause $1 billion in damages and control costs every year. While some species can easily spread upstream in unidirectional river environments, not all invasive species are able to do so. In a paper publishing on Thursday, May 25th in the SIAM Journal on Applied Mathematics, Qihua Huang, Hao Wang, and Mark Lewis present a continuous-discrete hybrid population model that describes the invasive dynamics of zebra mussels in North American rivers. "We wanted to develop and apply a mathematical model to understand the interaction between population growth and dispersal, environmental conditions, and river flow in determining upstream invasion success of zebra mussels," Huang said. Since its introduction to North America in 1986, the zebra mussel has invaded several large rivers, including the Mississippi, Hudson, Ohio, and St. Lawrence. "Rivers are key natural resources, and once zebra mussels invade the consequences can be disastrous," Lewis said. "Not only are the rivers themselves affected, they can spread the zebra mussels to new downstream locations." The mussels consume algae that is otherwise meant for native fish populations, and are considered unsafe for human consumption because they accumulate pollutants and toxins when filtering. Three main phases--larvae, juveniles, and adults--characterize the mussel's life cycle. Larvae are planktonic, and drift through the water for a few days or weeks before setting on a surface and activating the juvenile stage. Upon sexual maturation in their second year of life, juveniles are considered adults and can reproduce once water temperatures are warm enough. "The larval life stage is relatively short compared to the zebra mussel lifespan," Huang said. "As a result, a model for the spread of zebra mussels in a river requires the introduction of different time scales." The authors chose to assume that settled larvae, juveniles, and adults all have the same survival rate. Zebra mussels' survival in North American rivers is contingent upon a myriad of physical, biological, and chemical factors, including--but not limited to--water temperature, flow rates, salinity, turbidity, and pH levels. They are most heavily affected by unidirectional water flow, which shifts river sediment, sweeps mussel larvae downstream, and inhibits attachment to the benthos - the river bottom. "The dynamics of unidirectional water flow found in rivers can play an important role in determining invasion success," Huang said. "The alteration of hydrodynamic regimes associated with water management has direct effects on river ecosystem dynamics." As a result, it is difficult for zebra mussels to spread upstream in high flow rivers. Because the zebra mussel has unusual dynamics, classical models do not suffice. Instead, the authors develop and employ a novel, impulsive, spatially-explicit population model. "In the model, the dynamics of the dispersing larvae stage are governed by an advection diffusion-reaction equation, while juvenile and adult growth are described by two difference equations that map the population density in the current year to the population density in the next year," Huang said. These equations combine the process-oriented population growth model with a hydrological model, based on available data about river flow dynamics. Past researchers have proposed three measures of population persistence that reflect reproductive output of zebra mussels. The measures denote the fundamental niche of the population, the source-sink distribution, and the net reproductive rate (R) -- the average number of adult mussels produced from a single adult throughout its lifetime. If R>1, a population will grow; if R"We determined conditions for persistence of zebra mussels in rivers as a function of temperature and flow rate," Huang said. "The population persists in a river only when the flow velocity is low and the water temperature is moderate. We found that the population cannot persist in a river if it is unable to spread upstream." The authors' successful model offers multiple opportunities for further analysis. For example, one could adapt the model to study other environmental factors that affect population persistence, such as seasonality. "The living conditions for an invasive species and the hydrodynamics environment in a river can vary seasonally," Huang said. "The theory developed here could be extended to more general models by including seasonal variations in population growth and temporal variations of flow rate." Additionally, the active nature of rivers makes them prone to variable landscapes and inconsistencies. "Deep pools and shallows in a river are examples of heterogeneities that typically occur on shorter spatial scales than the whole stretch of a river," Huang said. "It would be interesting to further investigate how the heterogeneous landscapes affect the successful invasion of zebra mussels." The authors believe that these heterogeneities might make it possible for zebra mussels to persist in rivers even without upstream spread. Finally, the researchers could use their hybrid model to monitor the dynamics of other invasive species in rivers, such as the quagga mussel (). "Quagga and zebra mussels possess similar morphologies, life cycles, and functional ecologies, but different sensitivities to environmental factors," Huang said. "Patterns of relative dominance and competitive exclusion amongst these species may vary over space and time. As a future effort, we plan to extend our single-species model to a competition model to understand how the interaction between flow rate and environmental factors impact the persistence, extinction, and competitive exclusion in rivers." Source article: A Hybrid Continuous/Discrete-Time Model for Invasion Dynamics of Zebra Mussels in Rivers. SIAM Journal on Applied Mathematics. To be published. Qihua Huang is a postdoctoral fellow in the Centre for Mathematical Biology and the Department of Mathematical and Statistical Sciences at the University of Alberta. Hao Wang is an associate professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. Mark Lewis is a Senior Canada Research Chair in Mathematical Biology and a professor in the Department of Mathematical and Statistical Sciences and the Department of Biological Sciences at the University of Alberta.


News Article | April 25, 2017
Site: phys.org

Traditionally, FRAP data have been used to measure molecular diffusion—the passive drifting of molecules within the jelly-like cytoplasm inside a cell. But these molecular movements aren't always so passive. In many cellular processes, molecules can be transported actively by molecular motors, which tow molecules around like locomotives dragging lines of freight cars. "We know that active transport is important in many cellular systems, but there wasn't any way to capture it from FRAP data," said Veronica Ciocanel, a Ph.D. student in Brown's Division of Applied Mathematics. "We've developed a modeling technique for FRAP data that includes active transport and can quantify details about how those dynamics work." In a paper published in the Biophysical Journal, Ciocanel and her colleagues demonstrated the technique by describing new details about how egg cells redistribute genetic material before they begin dividing to form an embryo. Getting more from FRAP To perform a FRAP experiment, scientists tag molecules that they want to observe with glowing fluorescent proteins. Then they zap the area of interest with a laser, which deactivates some of the fluorescent proteins and creates a small dark spot within the glowing mass. Then scientists watch as the dark spot dissipates, which happens gradually as darkened molecules drift out of the spot and still-fluorescent molecules drift in. The amount of fluorescence in the spot as time progresses is what's known as a recovery curve. The recovery curve can then be fed into a mathematical model that generates a diffusion coefficient, an average rate at which the molecules drift around. Some models can also tease out a binding rate (the rate at which molecules stop moving by attaching themselves to some other molecule or substrate), but there weren't any that could deal with active transport. Ciocanel set out to create one in collaboration with a lab led by Kimberly Mowry, a professor of biology at Brown. One of the things Mowry's lab studies is RNA localization in egg cells, or oocytes. Before dividing to form embryos, oocytes redistribute messenger RNA—critical genetic molecules—from near the nucleus of the cell to the outer membrane on one of the cell's sides. The process occurs across animal species and is essential to normal embryo development. Mowry's lab studies it in a frog species called Xenopus laevis because the species' oocytes are relatively large and easier to observe. Mowry and other researchers had shown that active transport via molecular motors, along with diffusion, was likely important to the localization process in Xenopus oocytes. There was also speculation that the transport wasn't unidirectional from the nucleus out to the membrane. Mowry had performed experiments suggesting that mRNA molecules actually move back toward the nucleus at times during the process. But it was impossible to capture all of those dynamics via FRAP. Working with Björn Sandstede, chair of Brown's Division of Applied Mathematics, Ciocanel developed models using sets of partial differential equations that could capture active dynamics. One model captured two states of molecular movement: simple diffusion as well as active transport in a single direction. A second more complex model captures diffusion, two-directional movement as well as the possibility that some molecules remain stationary for periods of time. Ciocanel then developed a set of numerical techniques to solve the model and give velocities for active transport motion. Once the models were created and could be solved numerically, Ciocanel ran them on synthetic FRAP data from a hypothetical system in which the contributions from active transport were known. She showed that the models could correctly reproduce the active dynamics from the synthetic data. Having validated the models, the researchers applied them to real data from FRAP experiments on Xenopus and were able to shed new light on the RNA localization process. "We were able to quantify the contributions from each of the mechanisms," Ciocanel said. "We can predict how much of the mRNA is diffusing, moving up and down or pausing along the way." The models were also able to confirm small but important nuances in the dynamics. For example, the research showed that bi-directional transport occurred more prominently in the part of the cell closest to the membrane. New insights like these could ultimately help scientists to get a more complete picture of the dynamics at play in this critical cellular process. But this is far from the only setting where the technique could be helpful. Active transport is known to occur in many cellular processes. Synaptic activity in the brain, for example, is thought to involved active mRNA localization. "Whenever there's active transport," Sandstede said, "this method allows you to learn about what's happening." Explore further: Barriers and molecular trains trap Joubert syndrome protein in cilia


News Article | April 25, 2017
Site: www.eurekalert.org

PROVIDENCE, R.I. [Brown University] -- Understanding how proteins and other molecules move around inside cells is important for understanding how cells function. Scientists use an experiment called Fluorescence Recovery after Photobleaching, or FRAP, to investigate this molecular motion, and now Brown University researchers have developed a mathematical modeling technique that makes FRAP much more useful. Traditionally, FRAP data have been used to measure molecular diffusion -- the passive drifting of molecules within the jelly-like cytoplasm inside a cell. But these molecular movements aren't always so passive. In many cellular processes, molecules can be transported actively by molecular motors, which tow molecules around like locomotives dragging lines of freight cars. "We know that active transport is important in many cellular systems, but there wasn't any way to capture it from FRAP data," said Veronica Ciocanel, a Ph.D. student in Brown's Division of Applied Mathematics. "We've developed a modeling technique for FRAP data that includes active transport and can quantify details about how those dynamics work." In a paper published in the Biophysical Journal, Ciocanel and her colleagues demonstrated the technique by describing new details about how egg cells redistribute genetic material before they begin dividing to form an embryo. Getting more from FRAP To perform a FRAP experiment, scientists tag molecules that they want to observe with glowing fluorescent proteins. Then they zap the area of interest with a laser, which deactivates some of the fluorescent proteins and creates a small dark spot within the glowing mass. Then scientists watch as the dark spot dissipates, which happens gradually as darkened molecules drift out of the spot and still-fluorescent molecules drift in. The amount of fluorescence in the spot as time progresses is what's known as a recovery curve. The recovery curve can then be fed into a mathematical model that generates a diffusion coefficient, an average rate at which the molecules drift around. Some models can also tease out a binding rate (the rate at which molecules stop moving by attaching themselves to some other molecule or substrate), but there weren't any that could deal with active transport. Ciocanel set out to create one in collaboration with a lab led by Kimberly Mowry, a professor of biology at Brown. One of the things Mowry's lab studies is RNA localization in egg cells, or oocytes. Before dividing to form embryos, oocytes redistribute messenger RNA -- critical genetic molecules -- from near the nucleus of the cell to the outer membrane on one of the cell's sides. The process occurs across animal species and is essential to normal embryo development. Mowry's lab studies it in a frog species called Xenopus laevis because the species' oocytes are relatively large and easier to observe. Mowry and other researchers had shown that active transport via molecular motors, along with diffusion, was likely important to the localization process in Xenopus oocytes. There was also speculation that the transport wasn't unidirectional from the nucleus out to the membrane. Mowry had performed experiments suggesting that mRNA molecules actually move back toward the nucleus at times during the process. But it was impossible to capture all of those dynamics via FRAP. Working with Björn Sandstede, chair of Brown's Division of Applied Mathematics, Ciocanel developed models using sets of partial differential equations that could capture active dynamics. One model captured two states of molecular movement: simple diffusion as well as active transport in a single direction. A second more complex model captures diffusion, two-directional movement as well as the possibility that some molecules remain stationary for periods of time. Ciocanel then developed a set of numerical techniques to solve the model and give velocities for active transport motion. Once the models were created and could be solved numerically, Ciocanel ran them on synthetic FRAP data from a hypothetical system in which the contributions from active transport were known. She showed that the models could correctly reproduce the active dynamics from the synthetic data. Having validated the models, the researchers applied them to real data from FRAP experiments on Xenopus and were able to shed new light on the RNA localization process. "We were able to quantify the contributions from each of the mechanisms," Ciocanel said. "We can predict how much of the mRNA is diffusing, moving up and down or pausing along the way." The models were also able to confirm small but important nuances in the dynamics. For example, the research showed that bi-directional transport occurred more prominently in the part of the cell closest to the membrane. New insights like these could ultimately help scientists to get a more complete picture of the dynamics at play in this critical cellular process. But this is far from the only setting where the technique could be helpful. Active transport is known to occur in many cellular processes. Synaptic activity in the brain, for example, is thought to involved active mRNA localization. "Whenever there's active transport," Sandstede said, "this method allows you to learn about what's happening." The research was supported by the National Science Foundation (DMS-1408742) and the National Institutes of Health (GM071049).


News Article | April 25, 2017
Site: www.sciencedaily.com

Understanding how proteins and other molecules move around inside cells is important for understanding how cells function. Scientists use an experiment called Fluorescence Recovery after Photobleaching, or FRAP, to investigate this molecular motion, and now Brown University researchers have developed a mathematical modeling technique that makes FRAP much more useful. Traditionally, FRAP data have been used to measure molecular diffusion -- the passive drifting of molecules within the jelly-like cytoplasm inside a cell. But these molecular movements aren't always so passive. In many cellular processes, molecules can be transported actively by molecular motors, which tow molecules around like locomotives dragging lines of freight cars. "We know that active transport is important in many cellular systems, but there wasn't any way to capture it from FRAP data," said Veronica Ciocanel, a Ph.D. student in Brown's Division of Applied Mathematics. "We've developed a modeling technique for FRAP data that includes active transport and can quantify details about how those dynamics work." In a paper published in the Biophysical Journal, Ciocanel and her colleagues demonstrated the technique by describing new details about how egg cells redistribute genetic material before they begin dividing to form an embryo. Getting more from FRAP To perform a FRAP experiment, scientists tag molecules that they want to observe with glowing fluorescent proteins. Then they zap the area of interest with a laser, which deactivates some of the fluorescent proteins and creates a small dark spot within the glowing mass. Then scientists watch as the dark spot dissipates, which happens gradually as darkened molecules drift out of the spot and still-fluorescent molecules drift in. The amount of fluorescence in the spot as time progresses is what's known as a recovery curve. The recovery curve can then be fed into a mathematical model that generates a diffusion coefficient, an average rate at which the molecules drift around. Some models can also tease out a binding rate (the rate at which molecules stop moving by attaching themselves to some other molecule or substrate), but there weren't any that could deal with active transport. Ciocanel set out to create one in collaboration with a lab led by Kimberly Mowry, a professor of biology at Brown. One of the things Mowry's lab studies is RNA localization in egg cells, or oocytes. Before dividing to form embryos, oocytes redistribute messenger RNA -- critical genetic molecules -- from near the nucleus of the cell to the outer membrane on one of the cell's sides. The process occurs across animal species and is essential to normal embryo development. Mowry's lab studies it in a frog species called Xenopus laevis because the species' oocytes are relatively large and easier to observe. Mowry and other researchers had shown that active transport via molecular motors, along with diffusion, was likely important to the localization process in Xenopus oocytes. There was also speculation that the transport wasn't unidirectional from the nucleus out to the membrane. Mowry had performed experiments suggesting that mRNA molecules actually move back toward the nucleus at times during the process. But it was impossible to capture all of those dynamics via FRAP. Working with Björn Sandstede, chair of Brown's Division of Applied Mathematics, Ciocanel developed models using sets of partial differential equations that could capture active dynamics. One model captured two states of molecular movement: simple diffusion as well as active transport in a single direction. A second more complex model captures diffusion, two-directional movement as well as the possibility that some molecules remain stationary for periods of time. Ciocanel then developed a set of numerical techniques to solve the model and give velocities for active transport motion. Once the models were created and could be solved numerically, Ciocanel ran them on synthetic FRAP data from a hypothetical system in which the contributions from active transport were known. She showed that the models could correctly reproduce the active dynamics from the synthetic data. Having validated the models, the researchers applied them to real data from FRAP experiments on Xenopus and were able to shed new light on the RNA localization process. "We were able to quantify the contributions from each of the mechanisms," Ciocanel said. "We can predict how much of the mRNA is diffusing, moving up and down or pausing along the way." The models were also able to confirm small but important nuances in the dynamics. For example, the research showed that bi-directional transport occurred more prominently in the part of the cell closest to the membrane. New insights like these could ultimately help scientists to get a more complete picture of the dynamics at play in this critical cellular process. But this is far from the only setting where the technique could be helpful. Active transport is known to occur in many cellular processes. Synaptic activity in the brain, for example, is thought to involved active mRNA localization. "Whenever there's active transport," Sandstede said, "this method allows you to learn about what's happening."


"Mike brings a remarkable amount of industry knowledge and expertise," said Raj Rajan, Chairman and CEO of SoftWear Automation. "We're thrilled to have him evangelize our technology to help redesign the textile and apparel industry supply chain." "The sewn products industry is ripe for innovation, and SoftWear Automation is perfectly positioned as a leader in this space," said Dr. Fralix. "I'm excited to join the team at this rapid stage of its growth. This is a fantastic opportunity to help lead the adoption of SoftWear's unique robots in a market I know so well." SoftWear Automation's Sewbots™ are currently commercially deployed in the home goods and automotive sectors. With Sewbots™ produced goods currently on retail shelves globally, SoftWear is committed to disrupting, in a positive way, the $100 billion sewn products industry. Sewbots™, fully autonomous sewing worklines, allow companies to SEWLOCAL™, geographically shortening the distance between consumers and manufacturers. An internationally recognized speaker, Dr. Fralix offers expertise on a variety of topics, including 3D product development, sizing for fit, production scheduling, industrial engineering, ergonomics, full package production, simulation, lean manufacturing systems, sustainable technologies, and the digital supply chain. He holds a Bachelor's degree in Applied Mathematics and Philosophy from North Carolina State University, a Master's degree in Business Administration from Duke University, and a Doctorate in Textile Technology Management from North Carolina State University, where he was appointed an Adjunct Associate Professor in 2009. He is also active in several industry organizations including the International Apparel Federation, where he serves as a member of the Board and Chairman of the Technical Committee. About SoftWear Automation, Inc. SoftWear Automation, Inc. is an Atlanta-based machine-vision and robotics startup disrupting the $100 billion sewn products industry by creating autonomous sewn products work lines in home goods, footwear and apparel. SoftWear's fully automated Sewbots™ allow manufacturers to SEWLOCAL™, moving their supply chains closer to the customer while creating higher quality products at a lower cost. For more information, visit www.softwearautomation.com. To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/dr-mike-fralix-to-work-with-softwear-automation-as-a-technology-evangelist-to-accelerate-the-deployment-of-sewbotstm-300452256.html


News Article | April 20, 2017
Site: www.chromatographytechniques.com

Ticks, and the Lyme disease they carry, have become a daily reality in much of North America and beyond. Repellent and detailed checks of every limb have become routine, even in suburbia. This year is expected to herald a bumper crop of blood-sucking arachnids to wooded areas of the Northeast U.S., according to some experts. Such predictions are based off a count of mice – another host species of ticks. But a complex new model incorporating a variety of other climate factors and life cycle phases of ticks can offer a new forecasting model for the regional spread of Lyme, for each season, as proposed by mathematicians at the Memorial University of Newfoundland. “Climate impacts tick survival mostly during nonparasitic periods of the life cycle,” said Xiunan Wang, one of the authors of the study in the Society for Industrial and Applied Mathematics Journal on Applied Dynamical Systems. “Seasonal variations in temperature, humidity, and resource availability have a strong effect on tick population dynamics.” The deer tick’s two-year life cycle is accounted for, over four stages: from egg, larva, nymph, to adult. The model incorporates mice and deer populations and their mortality rate, as well as the climate factors. It also incorporates the frequency of infection among the bloodsuckers, and even the bite rates of individual ticks. An eight-dimensional non-autonomous model with three different time delay results, said Wang, and his colleague, Xiao-Qiang Zhao, also at Newfoundland. Their model was tested at Long Point, a town in Ontario where the disease has taken off since 1980. The Canadian settlement was subject to the pandemic because of songbird transmission of ticks over long distances, they add. Their prediction: Lyme will continue to thrive in Long Point, due to climate predictions. But they offer solutions: clearing out ticks’ egg locations may help curb the spread in the Canadian hamlet. Destroying nests in attractive locations near human habitation could turn the tide, they added. “It may be helpful to regularly search for the spots where adult ticks usually lay eggs, like in sheds, in woodpiles, under rocks and in the crevices of walls,” said Wang. “Since tick eggs are static, it is more feasibly to focus on the clearance of eggs than to think about killing ticks of the other three life stages.” The prevailing prediction for this season, put forth by a husband-and-wife team, was covered widely by NPR and other media outlets last month. The New York-based pair based their forecast off mice counts from the year before. The theory is based off the fact that mice are the most efficient transmitter of the Borrelia burgdorferi bacteria – and a population explosion from one year will result in a bumper crop of ticks the following year.


News Article | May 8, 2017
Site: www.prnewswire.com

The two reached out to two members of ecodads.org, Steve Lewis (aka Mathomaticus), a mathematician who had taught 5-12th grade math, and is currently acquiring his Ph.D. in Mathematics Education from the Ohio State University, and Anthony Bear, who had a Masters in Applied Mathematics from Ohio University and had spent 5 years as a Math Educator in private schools. Collectively, they created an app called MyMathCards (http://www.mymathcards.com), now available for free on iTunes as iPad and iPhone apps. Dylan smiles and nonchalantly proclaims, "MyMathCards puts 400 flash cards into the palm of your hand. And this app goes up to computations with 20." The cards offer a beginning 1-12 set and an advanced calculation set 13-20 for those who already know their 1-12 computations. The cards can be flipped by double tapping them and advanced by swiping to the left. They have practice, random and test levels. Results can be sent via text, email or posted to Facebook, Twitter or on other social channels. Lewis adds, "This app is aligned with the Common Core Mathematics Standards to help each student obtain mastery in the four basic arithmetic operations. These skills lead to an easier road to enhance and develop their mathematical language." Lewis' white paper on the cards can be found here http://www.ecodads.org/mymathcards/Rationale_For_MyMathCards.pdf. This app can help students to return to math and science classes prepared for higher order problem-solving rather than being bogged down with basic practicing and drilling. To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/father-son-and-ecodads-team-up-to-create-free-arithmetic-flashcard-app-300452961.html


News Article | May 5, 2017
Site: www.prweb.com

Knowmail, an intelligent inbox assistant for professionals, has developed a secured and private enterprise solution using personalized artificial intelligence to help professionals focus on things that matter most, do more with less effort and balance work and life. Their AI privately learns user’s behaviors, habits, and preferences, and improves email communication within their various solutions: Microsoft Outlook, Cortana, and a standalone bot. This Artificial Intelligence recruitment round further expands their team and talents, skills, and experience, attending all facets of their solution’s needs. It helps improve its algorithm development and accuracy, capabilities and advantages in the arenas of machine learning, NLP, and deep learning, along with a push to re-release and expand its suite of solutions and offering. To gain the benefits of Knowmail for yourself, invites are open and accepted directly on the site per your platform of choice. Avi has ~20 years’ experience in software development, R&D and technology innovation including machine learning and NLP. His background includes founding startups, corporate positions such as Verint, as well as patent development. At Knowmail, his focus is AI for relevant personalization for the Information Worker, voice UI, as well as algorithm development. Yury has over 15 years of Statistics and AI experience in a variety of roles including hi-tech, government, and academics. His experience also includes Lead Data Scientist in the Israeli Police, and expanded research and instruction as a Lead researcher of the Central Bureau of Statistics. Within his background, he developed Machine Learning algorithms, led advanced statistical research and computational implementation, as well as lectured and trained within associated subjects. David is technologically proficient in Machine learning and AI, IoT, Internet and telecom, image and signal processing, software system architecture and engineering. His MA and PhD thesis were in dynamic neural networks for classifying non-stationary data. David's background consists of his position of VP Technology at Qualcomm Innovation Center, including CTO, VP R&D, Chief Architect, and more within various reputable companies and start-ups. Mark has a PhD in Applied Mathematics and covers over 40+ years’ mathematics, scientific computing, and algorithm development background. His experiences include: numerical methods and computer algorithms of function approximation and optimization, optimal electric filters design, computer aided geometric design, image and speech processing, computer vision, scientific findings publications, and more. A statistician and developer, Meir has been Knowmail’s data scientist from day one. He studies communication habits, builds data-oriented models including further optimizing and expanding per results and growing needs. He has grown from data research into intelligence modeling and algorithm programming. With over 20 years of software development and engineering experience, Alex has vast background in numerous programming languages, algorithm creation including mobile development. Within his broad work experience are positions at Rafael Advanced Defense Systems and Elbit Systems Ltd. The key to a trustworthy and personalized artificial intelligence which can understand users’ behaviors relies on the ability to combine NLP, cognitive & social analytics, low-level algorithm development, and pure math. Knowmail’s AI team includes talented engineers who correspond to such a need and collaborate to achieve the vision of helping professionals stay focused and do more at work with less effort and stress. You may find more information on Knowmail at https://www.knowmail.me as well as their Facebook, LinkedIn and Twitter.

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