|Publication number||US5971001 A|
|Application number||US 08/865,531|
|Publication date||Oct 26, 1999|
|Filing date||May 29, 1997|
|Priority date||May 29, 1997|
|Publication number||08865531, 865531, US 5971001 A, US 5971001A, US-A-5971001, US5971001 A, US5971001A|
|Inventors||Bo Goran Andersson|
|Original Assignee||Dresser Industries, Inc.|
|Export Citation||BiBTeX, EndNote, RefMan|
|Patent Citations (39), Referenced by (40), Classifications (9), Legal Events (5)|
|External Links: USPTO, USPTO Assignment, Espacenet|
The present invention relates to a fitting assembly and a method for tapping into a fluid flow conduit, and, more particularly, to such an assembly and method which permits access to fluid in the conduit.
When it is desired to add another component, such as a conduit, a measurement device, or the like, to an existing hydraulic equipment installation, one or more fluid flow conduits of the installation must often be replaced which is expensive, time consuming and requires the fluid flow to be shut off. This problem is compounded in installations, such as gasoline service stations, or the like, in which there is limited access to the conduits that connect the fuel dispensers and pumps.
Fitting assemblies have evolved that enable a component to be retro-fitted to a fluid flow conduit. However, these existing assemblies are often costly, bulky and/or complex with a relatively large number of parts. Also, they can be difficult to install and/or are not adaptable to a wide range of installations. Further, in the case of gasoline service stations, these prior art assemblies often cause significant fuel and gasoline vapor losses to the atmosphere.
Therefore, there is a need for a self-tapping fitting assembly of the above type which can easily and quickly be tapped into an existing conduit to enable a component to be connected to the conduit without having to disconnect, break or replace the conduit. There is a further need for a fitting of the above type which can be retrofitted to an existing fluid flow system, yet is cost efficient, and compact. There is a still further need for a fitting of the above type which requires relatively few parts, shorter installation time and enhanced retrofit flexibility, with a minimum of fuel and vapor losses to the environment. There is a still further need for a fitting assembly of the above type which provides efficient interaction between the fluid and the component that is added.
The present invention, accordingly, provides a system and method which solves the above problems of existing fitting assemblies. To this end, a fitting assembly and method is provided in which a clamp having a threaded port is secured around the conduit. A threaded bushing is inserted into the threaded port and has an axial bore that guides and supports a tool when the tool is inserted through the bore to pierce the corresponding surface of the conduit. After the tool and the bushing are removed from the threaded port, the component to be added is threadably connected into the threaded port and has access to the fluid flowing through the conduit. According to one embodiment of the invention, the component to be added is a sensor which accesses the fluid through the pierced conduit and senses a characteristic of the fluid.
Thus, a major advantage is achieved with the assembly and method of the present invention since the assembly can be retrofitted to an existing fluid flow system, yet is cost efficient and compact. Also, the assembly and method of the present invention requires relatively few parts, shorter installation time and enhanced retrofit flexibility, and, in the case of gasoline service stations, minimizes fuel and vapor losses to the environment. Further, the assembly and method of the present invention permits efficient access to the fluid flowing through the conduit.
FIG. 1 is an isometric, exploded view of the fitting assembly of the present invention.
FIG. 2 is an isometric view similar to FIG. 1, but depicting the fitting assembly of FIG. 1 connected to a fluid flow conduit.
FIG. 3 is a view similar to FIG. 2 but depicting a sensing unit installed on the fitting assembly of FIGS. 1 and 2.
FIG. 4 is a cross-sectional view of the fitting assembly and the installed sensing unit of FIG. 3.
Referring to FIGS. 1 and 2 of the drawings, the reference numeral 10 refers, in general, to the fitting assembly of the present invention which is designed to clamp around a conduit 12 containing fluid, such as gasoline, gasoline vapor and/or the like. The assembly 10 includes a clamp assembly 14 having a member 14a and a member 14b which together extend around the outer circumference of a selected section of the conduit 12.
The clamp member 14a is formed by a body member 16 having a threaded port 16a extending therethrough, and two flanges 18a and 18b extending from diametrically opposed sides thereof. The lower surface of the body member 16, as viewed in FIG. 1, defines a concave surface that corresponds to the curvature of the corresponding portion of the conduit 12 (which in the example illustrated is the upper portion).
The clamp member 14b is formed by a curved plate 20 defining a concave surface corresponding to the curvature of the corresponding lower portion of the conduit 12. A pair of flanges 22a and 22b extend from opposed sides of the plate 20. The flanges 18a and 22a, as well as the flanges 18b and 22b, have respective openings that align when the clamp member 14a and the clamp member 14b are placed around the corresponding surface of the conduit 12 as shown in FIG. 2. A bolt 24a extends through the aligned openings 18a and 22a, and a bolt 24b extends through the aligned openings 18b and 22b to secure the clamp assembly 14 over the corresponding circumferential portion of the conduit 12. A gasket 26 extends between the concave surface of the body member 16 and the corresponding surface of the conduit 12 when the clamp assembly 14 is in its clamped position of FIG. 2, thus providing a compression seal on the conduit.
A threaded bushing 28 is provided that is designed to extend in the port 16a in threaded engagement therewith. An axial bore 28a extends through the bushing 28 and has a diameter that is less than the diameter of the port 16a and is adapted to receive a drill bit 30 with a minimum of clearance. A drill (not shown) is then utilized to apply a sufficient amount of torque to the drill bit 30 to drill through the corresponding surface of the gasket 26 and the conduit 12 while the drill bit 30 is guided and supported in the bore 28a.
After the conduit 12 is pierced in the above manner, the drill bit 30 is removed from the bushing 28 and the bushing is removed from the port 16a. Thus, access to the fluid flowing through the conduit 12 is provided through the port 16a and the pierced openings in the gasket 26 and the conduit 12, permitting a component, such as a conduit, a measuring device, or the like to be connected to the port 16a.
For the purpose of example, it will be assumed that it is desired to measure a characteristic of the fluid flowing through the conduit. To this end, a sensing unit, shown in general by the reference numeral 32 in FIGS. 3 and 4, is attached to the assembled clamp assembly 14. The sensing unit 32 is provided with a housing 34 in which electrical components (not shown) used in the sensing operation are disposed. Since these electrical components are conventional, they will not be described in any detail.
An externally threaded sleeve 36 projects downwardly from the housing 34, as viewed in FIGS. 3 and 4, and can be formed integrally with the housing or attached to the housing in any known manner. The sleeve 36 is sized so that it threadedly engages the port 16a of the body member 16 of the clamp member 14a. A probe 38 extends from the housing 34 and into the axial bore (FIG. 4) of the sleeve 36. The end of the probe 38 is located in the latter bore just above the pierced opening in the conduit 12. In this manner, the fluid from the conduit 12 permeates into the bore of the sleeve 36 and into contact with the probe 38. The probe 38 can be of a conventional design and, as such, is adapted to sense or measure one or more characteristics of the fluid flowing through the conduit 12. For example, the assembly 10 of the present invention is well suited to be installed on a vapor return conduit forming a portion of a gasoline dispensing/vapor recovery system such as the system disclosed in pending application Ser. No. 08/696,167 filed on Aug. 13, 1996, and assigned to the assignee of the present invention. In the latter environment, the probe 38 would be adapted to sense the amount of vapor flowing through the conduit 12 or, alternatively, the amount of oxygen in the air/vapor mixture flowing through the conduit.
In operation, the clamp assembly 14 is secured around the conduit 12 in the manner described above, with the gasket 26 extending between the clamp member 14a and the conduit. The threaded bushing 28 is then inserted into the relatively large-diameter port 16a of the body member 16 in threaded engagement therewith. A portion of the drill bit 30 is then inserted through the relatively small-diameter axial bore 28a of the bushing 28 with a minimum of clearance so that the drill bit is guided and supported in the latter bore. The drill bit 30 is used in the manner described above to drill a hole through the gasket 26 and the conduit 12, after which the drill bit is removed from the bushing 28, and the bushing is removed from the port 16a. Assuming that it is desired to sense a characteristic of the fluid flowing through the conduit 12, the threaded sleeve 36 of the sensing unit 32 is then inserted into the port 16a in threaded engagement therewith with the distal end of the probe 38 extending just above the pierced opening in the conduit 12. In this manner, the fluid from the conduit 12 permeates into the bore of the sleeve 36 and into contact with the probe 38 to enable the probe to sense a characteristic of the fluid in the conduit. The sensing unit 32 can be connected, in any known manner, to a control or recording system (not shown) for responding to the fluid characteristic sensed or measured by the probe 38, in any conventional manner.
The present invention thus enjoys several advantages. For example the threaded bushing 16 fits into the same-sized opening as the sensing unit 34 yet has a relatively small-diameter bore 16a for receiving the drill bit 30. The diameter of the bore 16a can be selected so that the drill bit 30 extends through the bore with a minimum of clearance and is thus guided and supported in the bore during the drilling operation. Also, the probe 38 is located outside the conduit 12 and thus does not interfere with the fluid flow through the conduit.
Also, the assembly of the present invention can be retrofitted to an existing fluid flow system, yet is cost efficient, and compact. Further, the assembly and method of the present invention requires relatively few parts, shorter installation time and enhanced retrofit flexibility, and minimizes fuel and vapor losses to the environment. Still further, the assembly and method of the present invention permits efficient interaction between the fluid and the sensor, or similar device.
It is understood that variations may be made in the foregoing without departing from the scope of the present invention. For example, rather than connect the sensing unit 34 to the port 16a, a conduit (not shown) having an externally threaded end portion can be secured to the port to divert at least a portion of the flow from the conduit 12. Also, as shown by the dashed lines in FIG. 4, the probe 38 can project downwardly through the sleeve 36, through the above-mentioned opening in the conduit 12 and into the interior of the conduit in the path of the fluid flowing through the conduit. Also the type of sensing unit, or probe, can vary within the scope of the invention, and can include a detector, and/or any type of fluid measuring device. Also, the drill bit used in the example described above can be replaced with a punch, or the like, for piercing the corresponding surface of the conduit. Of course, different bushings having bores with different diameters can be provided to accommodate drill bits, or other piercing, or punching, devices with different diameters.
It is understood that other modifications, changes and substitutions are intended in the foregoing disclosure and in some instances some features of the invention will be employed without a corresponding use of other features. Accordingly, it is appropriate that the appended claims be construed broadly and in a manner consistent with the scope of the invention.
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|U.S. Classification||137/15.12, 285/197, 73/866.5, 137/318|
|Cooperative Classification||Y10T137/6123, Y10T137/0458, F16L41/06|
|May 29, 1997||AS||Assignment|
Owner name: DRESSER INDUSTRIES, INC., TEXAS
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:ANDERSSON, BO-GORAN;REEL/FRAME:008586/0838
Effective date: 19970521
|Feb 11, 2002||AS||Assignment|
Owner name: DRESSER, INC., TEXAS
Free format text: CHANGE OF NAME;ASSIGNOR:DRESSER EQUIPMENT GROUP, INC.;REEL/FRAME:012598/0264
Effective date: 20010328
Owner name: DRESSER EQUIPMENT GROUP, INC., TEXAS
Free format text: NUNC PRO TUNC ASSIGNMENT;ASSIGNOR:DRESSER INDUSTRIES, INC.;REEL/FRAME:012598/0269
Effective date: 20020121
|May 14, 2003||REMI||Maintenance fee reminder mailed|
|Oct 27, 2003||LAPS||Lapse for failure to pay maintenance fees|
|Dec 23, 2003||FP||Expired due to failure to pay maintenance fee|
Effective date: 20031026


Oct. 21 (UPI) -- For the second time in five months, Israeli Prime Minister Benjamin Netanyahu said Monday he has failed to form a coalition government after a tight electoral contest with opposition leader Benny Gantz.
Netanyahu needed a coalition of 61 Knesset members to form a government, but said attempts to bring Blue and White Party leader Gantz to the negotiating table had failed "time after time."
"Ever since receiving the mandate [to form a government] I have worked relentlessly ... to establish a broad national unity government," Netanyahu said, announcing he was returning the mandate to Israeli President Reuven Rivlin.
Netanyahu's deadline to form a coalition was Wednesday. Rivlin said Gantz will now have 28 days to accomplish the task.
Gantz's party received 33 of 120 Knesset seats in Israel's Sept. 17 election, and Netanyahu's Likud Party won 32. Netanyahu had the backing of other parties, but not enough to form a government.
Netanyahu ordered last month's election in May after he wasn't able to form a coalition then, either.


Subject: I have found a CD that I think you would enjoy
Mysteries of Funk
Genres: Dance & Electronic, Pop, Rap & Hip-Hop, R&B
Similarly Requested CDs
Drum + Bass pioneer does well...
D. WILBORN | Dallas, TX | 09/22/2004
(4 out of 5 stars)
"I'm not a huge fan of drum and bass, but I am an obsessed music lover, and no matter what the genre a song is in, if its good, I'll listen. I've always heard of Grooverider and seen him on countless U.K. festival flyers, and decided to see what the fuss was all about. On Mysteries of Funk, Grooverider takes a very jazzy and lounge-inspired approach. Dancefloor destroyers "Rivers of Congo" and "Where's Jack the Ripper" roll along next to smoother, jazzier cuts such as "Imagination" the beautiful "Rainbows of Color". Emotive chords and clangy guitars phase in and out along extremely tight drum programming and crisp effects. The mastering is excellent, and the album comes through the speaker very clear and effortlessly. The whole album gives the listener a decent listen, and is essential for any newcomer to the drum and bass / jungle genre, as the excellent price and quality factor keep this album stocked in any decent music shop."


text:
One (1) Bridge Tally complete with one (1) artist black lead pencil 4B.
Custom designed and handmade folder containing 25 individual tally sheets.
Hand tied with black and white bakers twine complete with label. Fits in a purse.
Available in both Two Table and Three Table Progressive Bridge.
Measures 5.5" Wide x 6.5" Tall
Made from recycled materials and can be be recycled once used.
*Shipping Costs are extra - Please provide your address with zip code and/or postal code. We will forward the cost to you prior to payment.

domain: design


text:
I've had several requests recently for fat quarter quilt ideas. I know that my sewing machine has to go away while everyone's home for Christmas, but I thought that I would risk one more project before I tidy all my sewing gear away. The crossword puzzle quilt block is one of those enormous blocks that are great to make because you only need a few to make a decent sized quilt. It also fits the bill for a fat quarter quilt as it can be made from three fat quarters with a little left over that can be used in the binding.
The block can be sewn in three sections. The top five and bottom five rows are the same as each other. They each have a 4.1/2" white square at each end of the first row and a 6.1/2" white square in the middle of the first row. Everywhere else are 2.1/2" squares. The photo on the left shows the whole top section and the photo on the right shows half of the top section so that you can get a closer look at the placement of the squares. It's one of those quilt blocks where it's quite easy to misplace the squares if you're not concentrating. Make two of these panels.
The middle section is made using a 4.1/2" by 6.1/2" rectangle at each end and 2.1/2" squares everywhere else. As you can see, it's possible to use either 6.1/2" by 2.1/2" white rectangles or three 2.1/2" white squares above and below that white/red/white strip in the middle. The photo on the left shows half of the panel and the photo on the right shows the full panel.
When the three panels are complete, just sew them together as shown. It's a really eyecatching design and very simple to make - not a triangle in sight! I think that to continue the fat quarter theme I'll probably use different colours for a few more blocks, perhaps just keeping the white to give some continuity throughout the quilt. Each block is 26" square so you could make a 52" by 78" quilt with only six blocks.
And now I really must tidy the sewing machine and general sewing clutter out the way.
I hope that you all have a marvellous Christmas and a very Happy New Year.
Thanks for visiting my blog. Look forward to seeing you again.

domain: artistic


Artificial Intelligence (AI) has revolutionized almost every aspect of our lives, including security. With the advancements in AI technology, businesses and individuals can now better protect themselves from potential threats and have a more secure environment.
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In conclusion, the advancements in artificial intelligence have greatly enhanced security measures. Using AI for security provides businesses and individuals with a proactive and adaptive approach to protect their assets, data, and personal information from potential risks.
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With the advances in artificial intelligence (AI) for security, enhanced threat detection has never been more efficient and effective. AI brings a whole new level of intelligence to security systems, allowing for quicker and more accurate identification of potential threats.
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Security is a major concern for businesses and individuals alike. With the increasing complexity and sophistication of threats, it is crucial to have advanced solutions in place to protect against potential risks.
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Artificial Intelligence (AI) has revolutionized the field of security by providing enhanced incident response capabilities. By using AI in security operations, organizations can quickly and effectively detect and respond to security threats.
One of the key advantages of using AI in incident response is its ability to analyze large amounts of data in real-time. AI algorithms can process vast quantities of security logs, network traffic, and other relevant data sources to identify patterns and anomalies that may indicate malicious activity. This allows security teams to detect and respond to security incidents faster than ever before.
Furthermore, AI can help automate incident response workflows, freeing up security analysts to focus on more complex and strategic tasks. AI-powered systems can automatically triage and prioritize security alerts, handling routine tasks and low-level incidents. This enables security teams to allocate their resources more efficiently and respond to critical threats in a timely manner.
AI can also augment human decision-making by providing contextual information and recommendations. By analyzing historical incident data and threat intelligence feeds, AI systems can provide analysts with valuable insights and suggested remediation actions. This helps accelerate the incident response process and enhances the overall effectiveness of security operations.
In conclusion, leveraging AI technology in incident response can significantly improve an organization’s ability to detect, analyze, and respond to security incidents. By harnessing the power of artificial intelligence, organizations can enhance their security posture and protect their valuable assets and data from evolving cyber threats.
In the field of security, predictive analysis plays a crucial role in leveraging artificial intelligence. By analyzing vast amounts of data, AI algorithms can identify patterns, detect anomalies, and predict potential security threats. This allows security teams to proactively address emerging risks and prevent potential breaches.
Improved threat detection
One of the key benefits of predictive analysis in security is its ability to enhance threat detection. By analyzing historical data and identifying patterns, AI algorithms can identify indicators of compromise and anomalous behavior. This enables security teams to take timely action and prevent security incidents from escalating.
Proactive incident response
With the help of artificial intelligence, security teams can develop proactive incident response strategies. Predictive analysis allows organizations to analyze security events in real-time, detect unusual activities, and predict potential threats. By responding proactively, organizations can minimize the impact of security incidents and mitigate potential damage.
|Advantages of predictive analysis in security:
|1. Early detection of threats
|2. Proactive risk mitigation
|3. Enhanced incident response
|4. Improved overall security posture
Automation of Tasks
One of the key advantages of using artificial intelligence (AI) for security is the automation of tasks. AI systems have the ability to analyze vast amounts of data in real-time, allowing them to quickly detect and respond to potential threats. This automation saves valuable time and resources for security teams, who no longer have to manually sift through endless logs and alerts.
Using AI-powered intelligence, security systems can automatically process and prioritize alerts, based on predefined rules and patterns. This ensures that critical threats are immediately identified and addressed, while less urgent issues are handled in a timely manner. By automating these tasks, AI eliminates human error and increases the speed and accuracy of threat detection and response.
Furthermore, AI can continuously learn and improve its performance over time. It can adapt to new threats and evolving security challenges, making it an indispensable tool for staying ahead of the ever-changing threat landscape. This constant learning and adaptation enable AI to detect and prevent emerging threats before they can cause harm.
Overall, the automation of tasks through AI offers significant advantages for security. It empowers security teams to focus on higher-level decision-making and strategic planning, rather than getting overwhelmed by mundane and repetitive tasks. By leveraging the power of artificial intelligence, organizations can enhance their security posture, minimize risk, and protect their assets more effectively.
Reduced Human Error
Using artificial intelligence for security can significantly reduce human error. Unlike humans, AI systems are not subject to fatigue, distraction, or emotions that can impair their ability to detect and respond to security threats. AI-powered security systems can continuously monitor and analyze vast amounts of data, quickly identifying patterns and anomalies that may indicate a potential security breach.
In addition, AI algorithms can learn from past security incidents and improve over time, adapting to new threats and staying one step ahead of hackers and cybercriminals. By automating routine security tasks, AI can also free up human operators to focus on more complex and strategic security issues.
Overall, leveraging artificial intelligence in security operations can enhance the accuracy and efficiency of threat detection and response, reducing the risk of human error and strengthening overall security posture.
Artificial intelligence has transformed the field of security by introducing adaptive learning algorithms. These algorithms enable security systems to continuously learn, adapt, and improve over time, enhancing their ability to detect and respond to emerging threats.
With adaptive learning, security systems can analyze vast amounts of data in real-time, making it possible to detect patterns and anomalies that may indicate potential security breaches. By using artificial intelligence, these systems can quickly identify and respond to new and evolving threats, providing organizations with a proactive approach to security.
In addition to detecting and responding to threats, adaptive learning algorithms can also identify areas of weakness in existing security measures. By analyzing data from past incidents, these algorithms can identify patterns and trends that may indicate vulnerabilities in the system. This information allows organizations to strengthen their security protocols and ensure that they are prepared to defend against future attacks.
One of the key advantages of using artificial intelligence in security is its ability to learn from experience. Adaptive learning algorithms can analyze the outcomes of past security incidents and use this information to improve their future performance. This iterative learning process allows security systems to become more efficient and effective over time, constantly evolving to protect against new and emerging threats.
Overall, adaptive learning is a powerful tool that harnesses the capabilities of artificial intelligence to enhance security. By continuously learning and adapting, security systems can stay one step ahead of cybercriminals, ensuring the safety and protection of critical assets and sensitive data.
Using artificial intelligence (AI) in security enables organizations to effectively identify and mitigate anomalous behavior that could pose a threat. Anomaly detection utilizes advanced machine learning algorithms to analyze data and identify patterns of normal behavior within a given system or network.
By leveraging AI, security systems can automatically detect deviations from these established patterns, alerting security personnel to potential security breaches or unauthorized activities. AI-powered anomaly detection can monitor vast amounts of data in real-time, efficiently identifying anomalies that may go unnoticed by traditional security measures.
Intelligence derived from AI-powered anomaly detection enables organizations to proactively address security threats, minimizing the potential impact and reducing response times. By constantly learning from new data, the AI algorithms can adapt and improve their detection capabilities over time.
Artificial intelligence in security brings significant advantages, such as enhanced threat detection, early warning systems, and improved incident response. By effectively identifying and addressing anomalies, organizations can bolster their security posture and protect sensitive information and assets.
The use of AI in security not only improves the accuracy and speed of anomaly detection but also reduces the workload for security teams, allowing them to focus on more complex and strategic tasks. With AI-powered anomaly detection, organizations can stay one step ahead of potential threats, ensuring the overall safety and integrity of their systems and data.
Using artificial intelligence for security offers the remarkable advantage of continuous surveillance. AI-powered security systems can monitor and analyze data 24/7 without any breaks or distractions. This constant monitoring ensures that no potential threats or security breaches go undetected.
By using AI, security systems can process vast amounts of data in real-time, detecting patterns, anomalies, and potential risks. AI algorithms can analyze video feeds, detect unauthorized access attempts, and identify suspicious behavior. This level of continuous surveillance enables proactive response and quick action to mitigate any security threats.
Furthermore, AI-powered surveillance systems can adapt and learn from previous experiences. Sophisticated algorithms can improve their accuracy and efficiency over time, as they gather more data and learn from various security incidents. This continuous learning allows AI to become increasingly effective at identifying and preventing security breaches.
Continuous surveillance through AI not only enhances security measures but also provides peace of mind to individuals and organizations. With an AI-powered security system in place, there is a constant layer of protection that never sleeps, ensuring the safety and integrity of sensitive assets and information.
In conclusion, the use of artificial intelligence for security offers the invaluable benefit of continuous surveillance. AI-powered systems provide round-the-clock monitoring, quick response to threats, and continuous learning for improved accuracy. With AI, you can enhance your security measures and protect what matters most.
Proactive Security Measures
In today’s increasingly complex and interconnected world, businesses and organizations face growing challenges when it comes to ensuring the security of their data and assets. Traditional security measures may no longer be sufficient to protect against sophisticated threats, making the use of artificial intelligence (AI) an indispensable tool in the fight against cybercrime.
Enhanced Threat Detection
By using AI for security purposes, businesses can ensure better threat detection capabilities. AI algorithms can analyze massive amounts of data in real-time, identifying patterns and anomalies that may indicate a potential security breach. These proactive measures allow organizations to stay one step ahead of attackers and respond rapidly to emerging threats.
Artificial intelligence can automate routine security tasks, freeing up human resources for more strategic activities. This not only increases efficiency but also reduces the risk of human error. Machine learning algorithms can continuously learn from new data, improving over time and adapting to evolving threats. The use of AI provides organizations with the ability to perform security operations at unprecedented speeds and accuracy.
With the rapid advancement of technology, it is essential for businesses to stay up-to-date with the latest security solutions. By using artificial intelligence for security, organizations can implement proactive measures that ensure the protection of their valuable assets.
Scalability and Flexibility
Artificial intelligence (AI) has revolutionized the way we think about security, and one of its biggest advantages is scalability and flexibility. With AI, companies can easily scale their security solutions to match the needs of their growing business. Whether you are a small startup or a global enterprise, AI can adapt to the size of your organization and provide the necessary protection.
Traditional security measures often rely on manual processes and human intervention, which can be time-consuming and inefficient. In contrast, AI-powered security systems can quickly analyze and process vast amounts of data in real time, making them highly scalable. This means that as your business expands, AI can keep up with the increasing volume of threats and provide effective protection.
Moreover, AI offers a high degree of flexibility when it comes to security solutions. It can be integrated into existing systems and workflows without the need for major infrastructure changes. Whether you need to secure your on-premises infrastructure or your cloud-based applications, AI can be tailored to meet your unique security requirements.
AI can also adapt to the ever-evolving nature of cyber threats. As new threats emerge, AI algorithms can be trained to detect and respond to them effectively. This flexibility allows companies to stay ahead of cybercriminals and protect their data and assets from emerging vulnerabilities.
In conclusion, the scalability and flexibility of AI make it a powerful tool in the field of security. By harnessing the power of AI, companies can scale their security solutions to meet the needs of their business and easily adapt to the changing threat landscape.
Artificial intelligence (AI) for security offers significant cost-efficiency benefits. By using AI to detect and prevent security threats, businesses can save time and resources that would otherwise be spent on manual monitoring and detection.
AI systems can continuously analyze vast amounts of data in real-time, identifying patterns and anomalies that may indicate a potential security breach. This proactive approach allows businesses to address threats before they cause significant damage, reducing potential financial losses.
Additionally, AI-powered security systems can automate routine tasks, such as monitoring and responding to alerts, freeing up security personnel to focus on more complex and strategic security initiatives. This not only improves efficiency but also reduces costs associated with staff resources.
Furthermore, AI can help businesses optimize their security operations by providing valuable insights and recommendations. By analyzing historical data and identifying trends, AI algorithms can suggest improvements to security protocols, mitigating risks and enhancing overall security effectiveness.
Overall, leveraging artificial intelligence for security can provide cost-efficiency advantages, allowing businesses to allocate their resources more effectively and proactively protect against emerging security threats.
One of the key advantages of using Artificial Intelligence (AI) for security is its ability to provide data-driven insights. AI systems are capable of analyzing large amounts of data in real-time, allowing them to identify patterns, detect anomalies, and predict potential security threats.
By using AI for security, organizations can leverage the power of data to make informed decisions and take proactive measures to protect their assets. AI algorithms can analyze data from various sources, such as surveillance cameras, sensors, and user behavior, to detect and prevent security breaches.
By analyzing data in real-time, AI systems can quickly identify potential threats and alert security teams, enabling them to respond faster and more effectively. This data-driven approach allows organizations to stay one step ahead of hackers and other malicious actors.
Furthermore, AI can provide valuable insights into security trends and vulnerabilities. By analyzing historical data, AI algorithms can identify patterns and trends that might go unnoticed by human analysts. These insights can help organizations strengthen their security measures and develop more effective strategies to prevent future attacks.
Overall, leveraging AI for security provides organizations with data-driven insights that can enhance their security posture and protect against emerging threats. By using artificial intelligence in security operations, organizations can leverage the power of data to make smarter, quicker, and more informed decisions to keep their assets and sensitive information safe.
Faster Incident Response Time
Using artificial intelligence (AI) for security can greatly improve the response time to incidents. Traditional security methods often rely on manual processes, which can be slow and prone to errors. AI, on the other hand, can analyze large amounts of data in real-time and identify potential threats or abnormalities much faster than humans alone.
By utilizing AI for security, organizations can benefit from faster incident detection and response. AI-powered security systems can continuously monitor network traffic, user behavior, and system logs to identify any suspicious activities or patterns. This proactive approach allows organizations to respond to potential threats before they cause significant damage.
In addition to faster detection, AI can also accelerate incident response time. When an incident is detected, AI algorithms can automatically trigger predefined actions or alert security teams. This automation eliminates the need for manual intervention and reduces the time taken to initiate a response.
Furthermore, AI can assist in the analysis of incidents by providing real-time insights and recommendations. AI algorithms can quickly analyze the data associated with an incident and provide actionable intelligence to security teams. This can help them formulate an effective response strategy and minimize the impact of the incident.
|Advantages of using Artificial Intelligence for Security
|Faster Incident Response Time
Improved User Experience
In today’s fast-paced digital world, user experience plays a crucial role in the success of any product or service. By using artificial intelligence (AI) in security, companies can greatly enhance the user experience and provide a seamless and secure environment for their customers.
AI-powered security systems are able to analyze vast amounts of data in real-time, allowing them to quickly identify and respond to potential threats. This not only helps in keeping users’ personal information and data safe but also provides them with a sense of security and peace of mind.
With AI, security measures can be tailored to each individual user, providing a personalized experience. Through machine learning algorithms, AI systems can learn and adapt to user behavior, making accurate decisions on the fly. This means that users can go about their daily activities without constantly being interrupted by unnecessary security precautions.
Moreover, AI-powered security systems can also assist users in the event of a security breach. By continuously monitoring user activities and detecting any suspicious behavior, AI can quickly respond and take preventive measures to minimize the impact of an attack. This proactive approach not only protects users but also enhances their overall experience with the product or service.
Ultimately, the use of artificial intelligence in security helps in creating a more user-friendly and intuitive experience. With advanced technologies like facial recognition and voice authentication, users can securely access their accounts and make transactions with ease. AI-powered security solutions ensure that users can interact with digital platforms in a seamless and convenient manner, without compromising on security.
In conclusion, the integration of artificial intelligence in security not only bolsters the overall protection of users’ data but also helps in delivering an improved user experience. By leveraging the power of AI, companies can provide their customers with a seamless and secure environment, enhancing their confidence and trust in the product or service.
Advanced Threat Intelligence
When it comes to security, intelligence is crucial. Artificial intelligence (AI) has revolutionized the way we approach security, allowing us to tackle even the most advanced threats.
AI-powered threat intelligence systems leverage the immense power of machine learning and deep learning algorithms to analyze vast amounts of data in real-time. These systems are able to identify patterns, anomalies, and potential threats with incredible accuracy and speed.
By using AI in security, organizations can stay one step ahead of malicious actors and protect their assets and data effectively. With AI, security teams can:
- Enhance detection capabilities: AI-powered systems can detect and identify new and evolving threats that traditional security measures might miss. These systems continuously learn and adapt to the ever-changing threat landscape, providing real-time protection.
- Automate threat response: AI can automate the analysis and response to security incidents, allowing for faster incident response times. This automation frees up security personnel to focus on more strategic tasks and ensures a proactive approach to security.
- Improve accuracy: AI algorithms can analyze and correlate data from multiple sources, enabling security teams to gain a comprehensive view of potential threats. This improves the accuracy of threat identification and reduces false positives, allowing for more effective threat mitigation.
- Enable predictive security: By analyzing historical data and patterns, AI can predict and anticipate future security threats. This proactive approach enables organizations to take preventive measures and minimize the impact of potential attacks.
Overall, the use of artificial intelligence for security (AI in security) provides organizations with advanced threat intelligence capabilities that enhance their security posture. With AI-powered systems, organizations can mitigate threats, protect their sensitive information, and ensure the continuity of their operations.
In the field of artificial intelligence for security, self-learning systems play a crucial role. These systems are designed to continuously improve their performance and adapt to new threats and challenges.
Advantages of self-learning systems:
- Improved Detection: Self-learning systems have the ability to analyze large amounts of data and identify patterns that may indicate potential security threats. This allows for more accurate and efficient detection of security breaches in real-time.
- Adaptive Defense: These systems can automatically adjust their security measures based on the evolving nature of threats. They can learn from previous attacks and update their algorithms to better protect against similar attacks in the future.
- Efficient Resource Allocation: Self-learning systems can optimize the allocation of security resources by prioritizing the most critical areas that require protection. This helps in mitigating risks and maximizing the effectiveness of security measures.
- Reduced False Positives: By continuously learning and refining their algorithms, self-learning systems can minimize false positives, ensuring that only genuine security threats are flagged for investigation.
Overall, using self-learning systems in artificial intelligence for security provides organizations with a proactive and intelligent approach to safeguarding their assets and data from potential threats.
Streamlined Security Operations
In addition to enhanced security and threat detection capabilities, artificial intelligence (AI) technology is also revolutionizing security operations by streamlining processes and improving overall efficiency.
AI-powered solutions have the ability to analyze massive amounts of security data in real-time, allowing security teams to quickly identify potential threats and respond to incidents promptly. By automating routine tasks, such as monitoring security logs and analyzing network traffic, AI reduces the burden on security professionals, freeing up their time to focus on more critical tasks.
Moreover, AI can assist in identifying patterns and anomalies in data that may otherwise be missed by human operators. By leveraging machine learning algorithms, AI systems can continuously learn from the data they analyze, enhancing their ability to detect emerging threats and adapt to new attack vectors.
With AI technology, security operations centers can benefit from a more proactive and agile approach to security. By leveraging predictive analysis and automated response capabilities, security teams can detect and respond to potential security breaches before they cause significant damage.
Another advantage of using AI for security operations is the ability to integrate multiple security tools and systems into a unified platform. AI can consolidate data from various sources, providing security teams with a comprehensive view of their organization’s security posture. This integration not only simplifies security operations but also enables faster and more effective incident response.
Overall, the use of artificial intelligence in security operations brings numerous benefits, allowing organizations to stay one step ahead of cyber threats and safeguard their digital assets more effectively. By streamlining security operations and empowering security teams with advanced capabilities, AI is revolutionizing the way organizations approach security in the digital age.
Reduced False Positives
One of the major advantages of using artificial intelligence (AI) for security is the significant reduction in false positives. False positives occur when a security system incorrectly identifies a benign activity or object as a threat. This can lead to unnecessary alarm, wasted time and resources, and even damage to reputation and customer trust.
By leveraging AI, security systems can be trained to accurately distinguish between genuine threats and harmless activities or objects. AI algorithms analyze vast amounts of data and can detect patterns and anomalies that may indicate a potential security breach. This ability to accurately and efficiently detect threats helps to minimize false positives, allowing security personnel to focus their attention on real threats and respond more effectively.
AI-powered security systems excel in their ability to continuously learn and adapt to evolving threats. By constantly analyzing and updating their models based on new data, AI algorithms can improve accuracy over time. This means that as the system is exposed to more examples of benign activities, it becomes better at recognizing and filtering them out, further reducing false positives.
Reducing false positives not only increases the accuracy of security systems, but it also improves overall efficiency. By minimizing unnecessary alarm triggers, security personnel can allocate their time and resources more effectively. They can focus on investigating legitimate threats and quickly responding to mitigate risks. This improves the overall effectiveness of security operations and helps to prevent potential security breaches.
Furthermore, the reduced number of false positives minimizes the disruption caused by false alarms. This helps maintain business continuity and prevents unnecessary interruptions in operations. It also reduces the fatigue and frustration experienced by security personnel, allowing them to perform their tasks with greater efficiency and focus.
|Benefits of Reduced False Positives
|AI-powered security systems can accurately distinguish between genuine threats and harmless activities or objects.
|Minimizing false positives allows security personnel to allocate their time and resources more effectively.
|Better business continuity
|Reduced false positives help maintain business operations without unnecessary interruptions.
|Enhanced focus and productivity
|Reducing false alarms reduces fatigue and frustration for security personnel, enabling them to perform their tasks more efficiently.
Robust Defense Mechanisms
Artificial intelligence is revolutionizing the field of security by providing robust defense mechanisms against various threats and attacks. With the rapid advancements in technology, traditional security measures are often insufficient to protect sensitive data and valuable assets.
By using artificial intelligence for security, organizations can benefit from advanced algorithms and machine learning techniques that are capable of analyzing massive amounts of data in real-time. This enables the system to detect and neutralize threats quickly and efficiently.
One of the key advantages of using artificial intelligence in security is the ability to detect patterns and anomalies that may go unnoticed by humans. AI-powered systems can continuously monitor network activity, user behavior, and system logs to identify any suspicious activities or potential vulnerabilities.
Furthermore, artificial intelligence can adapt and learn from new threats, ensuring that the defense mechanisms are always up-to-date. Through continuous training and improvement, AI systems can stay one step ahead of the attackers and provide a proactive defense strategy.
Another benefit of using artificial intelligence for security is the reduction of false alarms. Traditional security measures often generate a high number of false positives, leading to alert fatigue and reducing the effectiveness of the system.
With AI-powered solutions, the system can analyze and filter out false alarms, allowing security teams to focus on genuine threats and take immediate action. This not only improves the efficiency of the security operations but also minimizes the risk of overlooking critical alerts.
In conclusion, artificial intelligence offers robust defense mechanisms for security by leveraging advanced algorithms, real-time analysis, and proactive threat detection. By using AI, organizations can enhance their security posture, protect sensitive data, and safeguard their operations from ever-evolving cyber threats.
Intelligent Access Control
One of the key advantages of using artificial intelligence (AI) for security is intelligent access control. Traditional security measures, such as locks and ID cards, can be circumvented or easily stolen. However, with AI-powered access control systems, the intelligence of the system ensures that only authorized individuals are granted entry.
Using AI in access control allows for advanced features that enhance security. For example, facial recognition technology can be used to identify individuals and grant access based on their unique facial features. This eliminates the need for physical ID cards or passwords, which can be lost or stolen.
The intelligence of AI can also be used to monitor and analyze access patterns. By tracking and analyzing data on who is entering and exiting a facility, AI can detect anomalies and identify potential security threats. This allows for proactive security measures to be taken, such as alerting security personnel or denying access to suspicious individuals.
AI-powered access control systems can also integrate with other security technologies, such as video surveillance and alarm systems. This integration allows for a more comprehensive and efficient security solution, where different components work together to provide a higher level of protection.
In conclusion, intelligent access control is a crucial application of AI in security. By using AI, organizations can enhance their access control systems, improve security measures, and better protect their assets and personnel.
|Advantages of using AI for security
|Intelligent access control
Efficient Resource Allocation
One of the key advantages of using artificial intelligence in security is its ability to efficiently allocate resources. Traditional security measures often require significant human involvement and can be time-consuming and costly. Artificial intelligence, on the other hand, streamlines the process by automating various tasks and optimizing resource allocation.
Automated Threat Detection
Artificial intelligence systems can continuously monitor and analyze vast amounts of data to detect potential security threats. By using advanced algorithms and machine learning techniques, these systems can quickly identify abnormal patterns or behaviors that may indicate a security breach. This automation greatly reduces the time and effort required for manual threat detection and allows security personnel to focus on addressing and mitigating the identified risks.
Optimized Security Response
With artificial intelligence technology, security teams can benefit from optimized resource allocation for responding to security incidents. By leveraging real-time data and machine learning algorithms, AI systems can prioritize and allocate resources based on the severity and potential impact of each incident. This ensures that the most critical threats receive immediate attention, while less urgent issues can be addressed in a timely manner without overwhelming security resources.
- Enhanced Efficiency: Artificial intelligence enables security teams to work more efficiently by automating repetitive tasks and allowing them to focus on higher-level strategic activities.
- Cost Savings: By automating processes and optimizing resource allocation, artificial intelligence can help reduce operational costs associated with security measures.
- Faster Response Time: AI systems can instantly respond to security incidents, minimizing response time and reducing the potential impact of breaches.
In conclusion, the use of artificial intelligence in security enhances resource allocation, improves efficiency, reduces costs, and enables faster response times. This technology empowers security teams to proactively protect against potential threats and mitigate risks effectively.
Integrating artificial intelligence (AI) into security systems brings numerous benefits to businesses not only for enhancing security but also for improving compliance with various regulations and standards.
By utilizing AI intelligence, organizations can ensure better compliance with industry-specific security requirements. AI algorithms are capable of analyzing massive amounts of data and identifying patterns and anomalies that might indicate non-compliance. This can help businesses stay ahead of potential security threats and take proactive measures to mitigate risks.
In addition, AI-powered security systems can automate compliance processes and streamline audits. By continuously monitoring and analyzing security data, they can generate automatic reports that demonstrate compliance with regulatory standards. This not only saves time and effort but also reduces the likelihood of human error or oversight.
Key advantages of using AI for enhancing compliance:
- Real-time monitoring: AI systems can monitor security events in real-time, enabling businesses to promptly address any compliance violations.
- Anomaly detection: AI algorithms can recognize unusual patterns or behaviors that do not comply with established security policies, allowing organizations to quickly identify and address potential vulnerabilities.
- Automated reporting: AI-powered systems can generate comprehensive reports that provide evidence of compliance with regulations, facilitating audits and improving transparency.
- Continuous improvement: AI can continuously learn and adapt to changing security threats and compliance requirements, ensuring businesses stay up to date with the latest standards.
With AI intelligence integrated into security systems, businesses can enhance their compliance efforts, reduce risks, and improve overall security posture. By leveraging the power of AI in security, organizations can achieve greater efficiency, accuracy, and peace of mind in their compliance endeavors.
Comprehensive Security Analysis
When it comes to ensuring the safety and protection of your valuable data, a comprehensive security analysis is of utmost importance. Artificial intelligence (AI) plays a critical role in enhancing security measures and minimizing risks.
By utilizing AI in security systems, organizations can benefit from advanced threat detection capabilities. AI algorithms can analyze vast amounts of data in real-time, identifying patterns and anomalies that may indicate a potential security breach. This technology enables proactive measures to be taken swiftly, preventing any detrimental impact.
In addition, AI-powered security systems offer improved accuracy and efficiency in identifying and responding to security incidents. Manual monitoring and analysis often prove to be time-consuming and prone to human errors. However, AI-driven solutions can continuously monitor network traffic, identify potential threats, and initiate appropriate actions with minimal human intervention.
Furthermore, AI can play a crucial role in securing confidential information and preventing unauthorized access. With AI-driven authentication systems, organizations can ensure that only authorized individuals gain access to sensitive data and resources. Additionally, AI algorithms can continuously learn and adapt to new attack techniques, providing enhanced protection against emerging threats.
Another crucial aspect of AI in security is its ability to optimize incident response and crisis management. AI-powered systems can prioritize security incidents based on severity, enabling a swift and efficient response. By automating the incident response process, organizations can minimize downtime and reduce the negative impact of security breaches.
In conclusion, utilizing artificial intelligence for security offers numerous advantages. Its ability to provide a comprehensive security analysis, enhance threat detection, improve accuracy, and streamline incident response makes it an indispensable tool in safeguarding valuable data and resources.
Reduced Response Time
In today’s fast-paced world, every second counts, especially when it comes to security. Utilizing Artificial Intelligence (AI) for security purposes can significantly reduce response time in critical situations.
AI-powered algorithms work tirelessly to analyze vast amounts of data, identify potential threats, and take immediate action. For example, an AI-powered security system can swiftly detect unauthorized access attempts, suspicious activities, or abnormal behavior patterns, enabling security personnel to respond promptly and effectively.
AI intelligence plays a crucial role in automating tedious and time-consuming security tasks, such as monitoring surveillance footage, analyzing logs, and detecting anomalies in network traffic. By offloading these tasks to AI systems, human operators can focus on more complex issues and urgent matters, ultimately leading to faster response times.
Furthermore, AI-powered systems can learn and adapt over time, continuously improving their detection accuracy and response capabilities. This continuous learning allows AI systems to stay one step ahead of potential threats and respond more effectively each time.
In summary, utilizing AI for security purposes has numerous advantages, one of which is reduced response time. The integration of AI intelligence allows for faster detection, analysis, and response to security incidents, ultimately enhancing overall safety and protection.
When it comes to security, artificial intelligence (AI) has revolutionized the way organizations protect their assets. With AI-powered systems in place, businesses can achieve an unprecedented level of protection against threats, both internal and external.
One of the key advantages of using artificial intelligence for security is its seamless integration into existing infrastructure. AI systems can be easily integrated into an organization’s security framework, allowing for a cohesive and comprehensive approach to protection.
By leveraging AI, businesses can harness the power of machine learning algorithms to analyze vast amounts of data in real-time, identifying patterns and anomalies that may signal a security breach. These AI systems can continuously learn and adapt, ensuring that security measures remain up to date and effective.
Improved Threat Detection
Artificial intelligence excels at detecting and responding to potential threats. By analyzing security data and identifying patterns, AI systems can quickly identify and mitigate risks before they escalate. This proactive approach to security minimizes the impact of threats and helps organizations stay one step ahead of attackers.
Another benefit of seamless integration with AI systems is the ability to streamline security operations. By automating repetitive tasks and processes, organizations can free up resources and manpower for more strategic activities. This not only improves operational efficiency but also enhances productivity and reduces costs.
Overall, the seamless integration of artificial intelligence into security operations offers numerous benefits. From improved threat detection to streamlined operations, AI-powered systems provide organizations with the tools they need to stay secure in an increasingly digital and connected world.
In an ever-changing and increasingly interconnected world, security is a paramount concern for both individuals and businesses. With the rapid advancements in artificial intelligence (AI) and its application to security, the future of secure systems has never been more promising.
AI has the capacity to revolutionize the field of security by enabling faster and more accurate threat detection and response. By leveraging the vast amounts of data generated in digital environments, AI-powered security systems can identify patterns, anomalies, and potential risks in real-time.
One of the key advantages of AI in security is its ability to continuously learn and adapt. Traditional security measures can quickly become outdated as new threats emerge. However, AI-driven systems can improve their accuracy and effectiveness over time, providing future-proof security solutions.
Another benefit of AI in security is its ability to automate repetitive tasks and processes. This allows security professionals to focus on more complex and high-risk activities, ultimately enhancing overall security capabilities.
Moreover, AI can enhance security in various sectors, such as network security, access control, and surveillance. AI algorithms can analyze network traffic, identify potential vulnerabilities, and respond to threats in real-time, reducing response times and minimizing potential damage.
The future of security lies in the combination of artificial intelligence and human expertise. While AI can provide powerful tools and insights, human intervention and decision-making remain crucial in complex security scenarios.
- AI is not a substitute for human security professionals but rather a powerful tool that can complement their skills and capabilities.
- The integration of AI into security systems opens up new possibilities for proactive and predictive security measures.
- As AI continues to evolve, the potential for enhanced security in various domains will only continue to grow.
- With the increasing sophistication of cyber threats, the adoption of AI-based security solutions is becoming increasingly necessary.
In conclusion, the future of security lies in harnessing the power of AI and integrating it into existing security systems. AI has the potential to provide future-proof security solutions by continuously learning, automating tasks, and enhancing overall security capabilities. By leveraging AI’s capabilities alongside human expertise, we can build a more robust and effective security ecosystem.


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Mini Wooden Hoop Earrings with Accent Color
This mini wooden hoop comes in a dark or light wood finish.
The wooden hoop itself is around 1 inch in diameter.
You choose the leather accent color.
The photo shows it in the lighter wood and our spring floral accent color.


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Tropical Smoothie Café will be adding two new Long Island locations within the next six months.
The Atlanta-based smoothie and sandwich shop with 340 locations nationwide has plans to open new stores at 285 South Broadway in Hicksville and 4250 Veterans Memorial Highway in Bohemia.
The Bohemia store, opening in September or October, will be a “nontraditional location,” according to company CEO Mike Rotondo. That location will be based in the lobby of an office building owned by Caf Management.
Chris Figoni of Caf Management said he put the deal together because of the lack of restaurants and food in the area.
“It’s mainly for the building,” Figoni said of Tropical Smoothie’s Bohemia location, “but it’s also certainly open to the public. They’re more than welcome to wander in.”
The Bohemia location still needs to receive approval from the Town of Islip, Figoni said.
With the two new locations, Long Island’s Tropical Smoothie count will be brought to 12. Another three franchise agreements have been put in place for the Island, although no locations have been decided on yet, Rotondo said.
“Long Island’s a great market for us. I couldn’t be prouder of what we’re doing there,” Rotondo said. “We foresee 30 to 40 locations there. We have only a few pockets around the country like Long Island that are being developed as aggressively.”

domain: economics


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Poster Session 24: Agro-Ecology
Wednesday, August 10, 5:00 PM - 6:30 PM, Exhibit Hall 220 A-E, Level 2, Palais des congrès de Montréal
Poster # 197. Cocke, Meagan*,1, Drinkwater, Laurie1, 1 Cornell University, Ithaca, NY, Shifts in soybean nitrogen fixation across a management-induced soil fertility gradient.
Poster # 198. Nesbitt, Johanna*,1, Adl, Sina*,1, 1 Dalhousie University, Halifax, Nova Scotia, Canada, Indicators of soil quality in organic and conventional potato fields in the Maritimes.
Poster # 199. Ahrens, Toby1, Matson, Pamela1, Fendorf, Scott1, 1 Stanford University, Stanford, CA, Spatially-variable ammonium fixation in an irrigated agricultural system in northwestern Mexico.
Poster # 200. Choi, Won Il*,1, Moorhead, Daryl 2, Neher, Deborah1, Ryoo, Mun Il3, 1 University of Vermont, Burlington, VT, USA2 University of Toledo, Toledo, OH, USA3 Korea University, Seoul, Korea, A modeling study of soil temperature and moisture effects on population dynamics of Paronychiurus kimi (Collembola: Onychiuridae).
Poster # 201. Drinkwater, Laurie*,1, Tonitto, Christina1, Cocke, Meagan, Vanek, Steven, 1 Department of Horticulture, Ithaca, NY, USA, Effect of management history and soil organic matter pool composition on microbially mediated N transformations.
Poster # 202. Dupuis, Eartha*,1, Whalen, Joann1, 1 Macdonald Campus of McGill University, Ste. Anne-de-Bellvue, Quebec, Canada, Soil microbial community responses to organic fertilizer applications in a wheat agroecosystem.
Poster # 203. Eriksen-Hamel, Nikita 1, Whalen, Joann1, 1 Macdonald Campus, Montreal, Quebec, Canada, Earthworm community dynamics in field enclosures under soybean production.
Poster # 204. Gao, Junxia1, 1, Li, Guifang*,, Liu, Zuoxin, 1 Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China, Identification and field control effect of Aspergillus flavus in maize field.
Poster # 205. Gentile, Roberta*,1, Nhamo, Pauline1, Vanlauwe, Bernard2, van Kessel, Chris1, Six, Johan1, 1 University of California, Davis, CA2 TSBF-CIAT, Nairobi, Kenya, Interaction between organic and mineral resource inputs on emissions of CO2 and N2O from different soil textures and residue qualities.
Poster # 206. Lee, Richard*,1, Wych, Joseph*,1, McConnaughay, Kelly, Morris, Sherri, 1 Bradley University, Peoria, IL, United States, Physiological and Growth Responses of Soybeans to Nighttime Carbon Dioxide Enrichment.
Poster # 207. Perreault, Jonahtan*,1, Whalen, Joann1, 1 Department of Natural Resource Science, Ste. Anne de Bellevue, Quebec, Canada, Earthworm cast production in a soybean agroecosystem.
Poster # 208. Ristaino, Jean1, Lui, Bo1, Hu, Shuijin1, Gumpertz, Marcia2, 1 Department of Plant Pathology, Raleigh, NC, 276952 Department of Statistics, Raleigh, NC, 27695, Influence of microbial species and functional diversity in soils on pathogen dispersal and ecosystem processes in organic and conventional agroecosystems.
Poster # 209. Robson, T. Matthew*,1, Lavorel, Sandra1, 1 Université Joseph Fourier, Grenoble, France, Response of nutrient cycling in a sub-alpine ecosystem to current and historic agricultural practices.
Poster # 210. Semenov, Alexander*,1, van Bruggen, Ariena1, Semenov, Alexander2, van Overbeek, Leo3, Termorshuizen, Aad1, 1 Biological Farming Systems, Wageningen, the Netherlands2 Dept. of Microbiology, Moscow, Russia3 Plant Research International B.V., Wageningen, the Netherlands, Influence of temperature fluctuations on Salmonella typhimurium and E. coli O157:H7 survival in manure.
Poster # 211. Van Bloem, Skip1, 2, Leakey, Andrew 3, Gonzalez Meler, Miquel4, Ort, Donald3, Long, Stephen3, Thomas, Richard 2, 1 University of Puerto Rico, Mayaguez, PR, United States2 West Virginia University, Morgantown, WV, United States3 Univerisity of Illinois, Urbana--Champaign, IL, United States4 University of Illinois, Chicago, Chicago, IL, United States, Effects of CO2 and drought on N2 fixation in field-grown soybean plants.
Poster # 212. Vukomanovic, Dragic*,1, Cutler, Joshua2, Dinon, Sarah2, Rasch, Jennifer2, Rajaniemi, Tara2, Scarano, Frank3, 1 Department of Chemistry and Biochemistry, North Dartmouth, Massachusetts, U.S.A.2 Department of Biology, North Dartmouth, Massachusetts, U.S.A.3 Department of Medical Lab. Science, North Dartmouth, Massachusetts, U.S.A, Phenazine antibiotics derived from environmental organisms: role in rhizosphere ecology and pathogen suppression.
Poster # 213. Wantai, Yu*,1, Shanmin, Shen*,, Lu, Zhang*,, Qiang, Ma*,, Shaohua, Zhao*,, 1 Institute of Applied Ecology of Chinese Academy of Sciences, Shenyang, Liaoning, China, Relationship between water-stable aggregates and soil nutrients in phaeozem after reclamation.
Poster # 214. Powell, Jeff*,1, Kuczynski, Eva1, Klironomos, John1, 1 University of Guelph, Guelph, ON, Canada, Is there a role for mycorrhizal fungi in genetically-modified, herbicide-tolerant cropping systems?
Poster # 215. Dauer, Joseph*,1, Mortensen, David2, Humston, Robert3, 1 The Pennsylvania State University, University Park, PA2 The Pennsylvania State University, University Park, PA3 Virginia Military Institute, Lexington, VA, The role of wind dispersal in spreading a glyphosate-resistant weed.
Poster # 216. Ishizuka, Daigo1, Horisaki, Atsushi2, Niikura, Satoshi2, Konuma, Akihiro*,3, 1 Graduate School of Science and Technology, Niigata, Niigata, Japan2 Tohoku Seed Co., Utsunomiya, Tochigi, Japan3 Plant Ecology Group, Tsukuba, Ibaraki, Japan, An applied ecological study on the F1 seed production of brassicaceous vegetables.
Poster # 217. Kim, Jae-Eun*,1, Hong, Sun-Kee2, Nakagoshi, Nobukazu3, 1 IDEC, Hiroshima University, Higashi-Hiroshima, Japan2 Forest Science Institute, Kookmin University, Seoul, Korea3 IDEC, Hiroshima University, Higashi-Hiroshima, Japan, Vegetation dynamics with rural landscape changes in East Asia.
Poster # 218. Kim, Sung Pil*,1, Oh, Yong Ju1, Han, Min Su1, Na, Young Eun1, Kim, Jin Ho1, 1 National Institute of Agricultural Science and Technology, RDA, Suwon, Rep.of Korea, The Quality of Water and Distribution of Vegetation.
Poster # 219. Mauchline, Alice1, Cussans, John2, Hurley, Rose, Brown, Valerie, Froud-Williams, Robert, 1 The University of Reading, Reading, UK2 Rothamsted Research, Harpenden, UK, Population dynamics of arable weeds: Extent of weed seed decline in cereal fields due to predation.
Poster # 220. Poggio, Santiago*,1, Ghersa, Claudio1, 2, Chaneton, Enrique1, 2, 1 Facultad de Agronomía, Universidad de Buenos Aires, Buenos Aires, Argentina.2 IFEVA-CONICET, Buenos Aires, Argentina., Test of species richness saturation in weed communities associated with functionally contrasting field crops.
Poster # 221. Prendeville, Holly*,1, Pilson, Diana 1, 1 University of Nebraska, Lincoln, NE, USA, Effects of virus-resistant transgenic squash on pollinator behavior.
Poster # 222. Schutte, Brian1, Regnier, Emilie 1, Harrison, S. Kent 1, 1 The Ohio State University, Columbus, OH, USA, Maternal plants as sources of emergence variation within Ambrosia trifida populations.
Poster # 223. Blackshaw, Rod*,1, Vernon, Bob2, 1 University of Plymouth, Plymouth, UK2 Pacific Agriculture Research Centre, Agassiz, British Columbia, Canada, Spatio-temporal stability of Agriotis lineatus and Agriotis obscurus populations in an agricultural landscape.
Poster # 224. Fleury, Dominique*,1, 2, Vincent, Charles1, Mauffette, Yves2, 1 Agriculture and Agri-Food Canada, Saint-Jean-sur-Richelieu, Québec, Canada2 Université du Québec À Montréal (ESI), Montréal, Québec, Canada, Seasonal abundance of Lygus lineolaris in Quebec vineyard and its effect on yield.
Poster # 225. Hernandez-Hernandez, Juan1, Castellanos, Ignacio*,1, Martinez-Morales, Miguel1, Moreno, Claudia1, Sanchez-Rojas, Gerardo1, Zuria, Iriana1, 1 Universidad Autonoma del Estado de Hidalgo, Pachuca, Hidalgo, Mexico, Carrion beetle distribution in an agricultural landscape in Hidalgo, Mexico: the role of forest remnants and food availability.
Poster # 226. Hicks, Helen*,1, Blackshaw, Rod1, Edgecombe, Victoria1, 1 University of Plymouth, Plymouth, UK, Spatial distribution of tipulid larvae in agricultural grassland.
Poster # 227. Homsher, Ryan1, Cady, Alan*,1, 1 Miami University, Oxford, OH, USA, Activity cycles and vertical stratification of spiders in cornfields.
Poster # 228. Rypstra, Ann*,1, Buddle, Christopher2, 1 Miami University, Hamilton, Ohio, USA2 McGill University, Montreal, Quebec, Canada, Silk reduces plant damage caused by pest insects.
Poster # 229. Sackett, Tara*,1, 2, Buddle, Chris1, Vincent, Charles2, 1 McGill University, Ste. Anne de Bellevue, Quebec, Canada2 Agriculture and Agri-Food Canada, Saint-Jean-sur-Richelieu, Quebec, Canada, Spider assemblages in apple orchards: how adjacent habitat structure influences potential source populations.
Poster # 231. Brandner, Thomas*,1, 1 Edgewood College, Madison, Wisconsin, Agrobiomes: A different way to think about sustainability.
Poster # 232. Javorek, Steve1, Antonowitsch, Rolfe2, Callaghan, Carolyn*,3, Grant, Matt1, Weins, Ted, 1 Agriculture and Agri-Food Canada, Kentville, NS, Canada2 Agriculture and Agri-Food Canada, Regina, SK, Canada3 Agriculture and Agri-Food Canada, Ottawa, ON, Canada, Assessing wildlife habitat capacity on farmland in Canada.
Poster # 233. Kroger, Robbie*,1, Holland, Marjorie1, Moore, Matt2, Cooper, Charlie2, 1 Department of Biology, University of Mississippi, University, MS, U.S.A.2 USDA-ARS, National Sedimentation Laboratory, Oxford, MS, U.S.A., Luxury uptake and nutrient flux of wetland plants within agricultural drainage ditches in Mississippi.
Poster # 234. Mishima, Shin-ichiro*,1, Taniguchi, Satoru, Komada, MIchio, 1 National Institute for Agro-Environmental Sciences, Tsukuba, Ibaraki, Japan, Life cycle assessment for agricultuer in mid-baisn scale in Japan.
Poster # 235. Paule, Audrey*,1, Pai, Aswini1, 1 St. Lawrence University, Canton, New York, USA, Field margins as reservoirs of biodiversity in agricultural landscapes in Chiang Dao, Thailand.
Poster # 236. Pisani Gareau, Tara, , Farmscaping with hedgerows in the central coast of California: Examining the potential for biological control.
Poster # 237. Powers, Michael*,1, 1 San Jose State University, San Jose, California, Vegetated filter strips: Using creeping wild rye (Leymus triticoides) to reduce agricultural non-point source pollution.
Poster # 238. Ren, Wei1, 2, Tian, Hanqin*,2, Liu, Mingliang2, Melillo, Jerry 3, Reilly, John3, 1 Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, PR China;, Beijing, China2 School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL, USA3 The Ecosystem Center, Marine Biological Laboratory, Woods Hole, MA, USA, Crop productivity and trace gas emission in agricultural ecosystems: The Agricultural Ecosystem Model (AEM) and its applications to China.
Poster # 239. Zuria, Iriana*,1, 2, Gates, J. Edward2, Castellanos, Ignacio1, 1 Centro de Investigaciones Biologicas, Pachuca, Hidalgo, Mexico2 Appalachian Laboratory, Frostburg, Maryland, USA, Species richness and abundance of birds in field margins of central Mexico: Local and landscape-scale effects.
All materials copyright The Ecological Society of America (ESA), and may not be used without written permission.

domain: biology


After traveling over 2000 km (1242 miles) on challenging asphalt roads in Tasmania over six days, the Lotus Cars Australia Exige Cup 260 has finished 12th.
While 12th certainly isn’t first, it’s worth noting that the Exige Cup 260 was completely unmodified from showroom spec save for a roll cage. The Exige’s performance in the rally against much more modified competition is testament to the brilliance of the stock Exige.
The Lotus was piloted by Sydney motoring journalist Dean Evans with Simone Bachmann as navigator.
We took this car from the Lotus showroom two weeks ago, put a cage in it, drove it to Tasmania, finished 12th outright and drove it home again without touching a thing. That’s why the Cup 260 is so good.
The Lotus also recorded a third fastest stage time and finished as the highest placed showroom spec car in the Modern category.
And here is the the official press release from Lotus Cars Australia.
LOTUS 12TH DOES THE DOZEN AT TARGA DEBUT
Cup 260 places 12th, and highest finishing Modern showroom spec car.
The Lotus Cars Australia Exige Cup 260 has finished 12th outright at Targa Tasmania’s gruelling six day event, set a third fastest stage time and is the highest placed showroom spec car in the Modern category.
Over 2000km and 39 stages, the Cup 260 showed its potential in the wet and dry by running without a single problem in arduous conditions, with nothing more than tyre pressure changes and fuel.
In showroom specification, the Cup 260 set some times that established it as a sports car able to compete with the very best supercars in the world – with a third fastest time on the Queenstown stage the event highlight by the crew of Dean Evans and Simone Bachmann.
“We took this car from the Lotus showroom two weeks ago, put a cage in it, drove it to Tasmania, finished 12th outright and drove it home again without touching a thing. That’s why the Cup 260 is so good,” enthused drive Dean Evans after the event.
Days four and five of Targa Tasmania saw plenty of action as the 270 strong field moved down Tasmanaia’s west coast to the overnight stop in Strahan, and then on to the finish in Hobart.
But with heavy rain and just three dry stages from the 13 stages, it was ‘advantage all-wheel drive’. That didn’t stop the Lotus from excelling in wet conditions though, relying on its ABS and traction control systems to record a string of top 10 times.
Crucially, on stages where the Lotus was to excel, such as the tight and twisting Hellyer Gorge, the wet weather played its hand with slick roads and treacherous conditions. However the Lotus quickly adapted and put in 11th fastest on the wet Mt Black stage, and 10th on the incredible 33km Rinadeena stage, with wet, dark and dangerous conditions that claimed many cars on the way into Strahan.
On the flowing morning of the final day, weather conditions were improving but still wet and the Lotus ranked 12th on the 33km Strahan, but it was Queenstown’s famous 6.4km stage up the moonscape that would be the Cup 260’s definitive stage.
“We always knew Queenstown would be our strongest stage,” explained Evans. “So when we saw the ‘WET’ board, it was a little frustrating.”
Despite the slick road, the Lotus climbed the mountain road and put in a stunning time to record 3rd outright stage time, passing a Porsche 911 GT3 and showing its true performance. “While it’s easy to speculate, I do think the Exige would have had a great chance to be fastest in the dry – but we’re still ecstatic with third!”
Onto Mt Arrowsmith, the event’s longest stage at 47.2km, the Cup 260 hit back again with a 7th fastest stage time, negotiating the tricky wet conditions without drama, moving into 12th spot and taking 15 seconds off two-time Bathurst winner Tony Longhurst in 11th.
Equal 10th fastest with Longhurst on the penultimate Tungatinah stage meant the dual was locked going into the final 6.6km Risdon Brook stage, and with 12 seconds to make up, it was made tougher by Longhurst pushing hard for 10th spot which was just two seconds ahead.
But the gap was too great, and though the Lotus made up five of those seconds to get within 7 seconds of 11th and 12 seconds of 10th, it placed the Lotus in the final position of 12th outright, after six days of competition.
Mechanically the Cup 260 is ready for another Targa. The tyres are only half worn, the brake pads barely worn at all, and it used less than 300 litres of fuel for the entire event.
Evans surmised: “In the wash-up, the intercom problem on day one was the difference between 10th and 12th. Simone did a superb job throughout, and while a top 10 was our target, we’re still extremely pleased with the top dozen.”
“Throw two days of wet conditions, and to still finish 12th on debut in a showroom spec car is a fantastic result that we’re very proud of. Especially the fact we drove it home after!”


The real estate unit of Ontario Municipal Employees Retirement System agreed to buy a 50% stake in the commercial real estate part of a $483-million London project for an undisclosed amount from Britain’s Crown Estate.
Crown Estate, the property company that generates income for Queen Elizabeth II, and Toronto-based Oxford Properties Group will own 150-year leasehold interests in two blocks at St. James’s Market between Piccadilly Circus and Trafalgar Square, the London-based property company said in a statement today.
The project “will deliver the most ambitious redevelopment in St. James’s in the last century and represents a major tipping point for our strategy for the area,” Paul Clark, TheCrown Estate’s investment and asset management director, said in the statement.
The Crown Estate, owner of about 50% of St James’s, has been using joint ventures to help rejuvenate its central London holdings. It sold 25% of London’s Regent Street for about 448-million pounds in 2010.
The venture with Oxford Properties will develop 210,000 square feet (19,500 square meters) of office space, 50,000 square feet of stores and restaurants as well as a new public square.


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