Tim Hembry of Bulldog Capital Ltd Utilises Bonds and Financial Instruments to Create Liquidity for Oil, Gas and Minerals Exploration and Infrastructure

Miller has several decades of experience in financial accounting and operations.

The Enterprise Technology Management Association Giving Back award supports ETMA's mission of enhancing the category image for industry participants and humanizing its members.

Certification opens new markets for Radeus Labs Satcom products.

Get a free set of LED micro race cars with the purchase of select RC cars or boats

Fast Pest Control has introduced a new interface for their website to make more user friendly. Now customers can book pest control services in just 45 seconds.

By Maggie White, A CPS Blogger.

To meet growing demand, Diversified Plastics, Inc. has enlarged its molding and packaging, environmentally controlled, room by 2,775 square feet (257.81 square meters).

Controllink a full-service Systems Integration and Automation solutions provider.

Both employees and company senior executives are struggling to understand how to handle the projected 40 percent increase in health care costs over the next eight years.

Bringing you a sneak peek of our new booth and Airsweep® demo

Full Motion, DC Control System Solutions

Hackathon competition to find innovative solutions for controlling epidemics like Coronaviruses, SARS, MERS, Ebola, and Zika organized by Somerset, NJ based Thomas Edison Energy Smart Charter School.

New versions of companion apps for Jaguar and Land Rover owners were released to App Store and Google Play after a major redesign.

This sanitization treatment is effective at dramatically reducing the number of bacteria and viruses from common colds, Coronavirus, Hepatitis B, Hepatitis C, Influenza A, H1N1 Virus and more!

Viking Pest Control introduces the future of pest control to North America

The easy-to-navigate website provides users with educational content on pest throughout New Jersey, Pennsylvania, Delaware, and the Eastern Shore of Maryland.

GRE Alpha expands their popular line of LED power supplies

Innovator & Multi-Genre Composer, Blythe Abigail 黄淑仁 Schulte, Gets Inspiration from Public-Private Partnerships Amid COVID-19 Crisis

Cisco’s Talos threat intelligence and research group revealed on Wednesday that one of its researchers discovered a critical remote code execution vulnerability in the CODESYS Control SoftPLC industrial controller software.

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#ad #TacoBellPartner First we show off Taco Bell's new Eclipse Xbox One X giveaway bundle (with the new Xbox Elite Series 2 Controller!), and then we discuss Obsidian's new RPG The Outer Worlds with reviewer Dan Stapleton. Plus: Star Wars Jedi: Fallen Order impressions & more!

The book Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing by Ron Kohavi (Microsoft, Airbnb), Diane Tang (Google) and Ya Xu (LinkedIn) is available for purchase, with the authors proceeds from the book being donated to charity.

Elizabeth Grace Saunders, founder and CEO of Real Life E and author of "The 3 Secrets to Effective Time Investment."

Moshe Cohen, a senior lecturer at Boston University's Questrom School of Business, says you can't take the emotion out of a negotiation. After all, negotiations revolve around conflict, risk, and reward — which are inherently emotional. Instead of sidelining your feelings, understand them. Cohen explains how to understand your triggers and use your emotions and those of your counterparts to your advantage.

Ed Stack, the chief executive of Dick's Sporting Goods, decided after the Parkland school shooting to pull assault rifles and high-capacity magazines from all of his company’s stores. The controversial choice hurt revenues. But the retailer weathered the storm, thanks to inclusive and thoughtful decision-making, careful communication with all stakeholders, and a strategic shift to new product lines. Stack explains why he chose to take such a public stance on a hot-button social issue and how it has affected him personally and professionally. He is the author of "It's How We Play the Game: Build a Business. Take a Stand. Make a Difference."

Margaret Parker, MD, FCCM, speaks with Eliotte L. Hirshberg, MD.

Margaret Parker, MD, MCCM, speaks Mark Duffett, PhD, about the article, High-Quality Randomized Controlled Trials in Pediatric Critical Care: A Survey of Barriers and Facilitators, published in the May 2017 issue of Pediatric Critical Care Medicine.

Defence Minister Rajnath Singh on Friday inaugurated the road link from Dharchula, a town in Uttarakhand's Pithoragarh district, to the Lipulekh pass, which is located at an altitude of 17,060 feet at the border between India and China.

Grand Canyon, Ariz. – The New Water Fire, on the South Rim of the Grand Canyon near Meadview, Arizona, was declared “controlled” on Friday, August 19. https://www.nps.gov/grca/learn/news/2011-08-24_new-water.htm

We conducted an experimental study of infection, transmission, and persistence of a nucleopolyhedrovirus (NPV) of Douglas-fir tussock moth (Orgyia pseudotsugata) to better understand mechanisms determining the efficacy of the virus when it is used as a microbial control agent. In a field experiment, we quantified infection rates of larvae exposed to either Tussock Moth Biocontrol-1, the strain currently used for control by the U.S. Forest Service, or a wild-type strain isolated from a natural population. We first allowed each pathogen to decay on experimental branches for 0, 1, or 3 days before allowing uninfected larvae to feed on the branches, and then we fit both a generalized linear model and an epidemiological model of virus transmission to the infection data. Longer decay of the NPV resulted in lower infection rates, but evidence that overall virus transmission differed between wild and pesticide isolates of NPV was weak. The short persistence time of the virus suggests that it does not last long on foliage, in turn suggesting that application of TM Biocontrol-1 must be carefully timed to ensure maximum mortality.

Vimeo video select list with external play/pause/rewind controls and fallback for iOS

A CSS only method of hiding the control bar on Vimeo videos

A tutorial to replace the default controls on a Vimeo video with a Play / Pause button, and make responsive.

We evaluated the effects of vegetation control and organic matter (OM) removal on soil water content (SWC) in a Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) plantation from age 3 through age 5. Treatments were presence versus absence of vegetation control through year 5 and bole-only harvest of the previous stand versus total-tree harvest of the previous stand including removal of all coarse woody residues.

If you own an AirPort base station, you can use the Timed Access feature to control the days and times when users access the Internet. This could come in handy in a variety of situations. For example, if you own a cafe and provide free wi-fi access, you can configure the AirPort to block all access to the Internet when your business is closed. And if you have children, you can set time limits for specific devices in your home.

There are two ways to use the timed access feature. You can create a default allow policy to allow all devices to access the Internet at any time, and then specify custom schedules for specific devices. Or you can create a default deny policy to prevent all devices from accessing the Internet according the schedule you specify, and then exempt specific devices by creating custom schedules.

Here's how to control AirPort network access with time limits:

1. Open the AirPort Utility application. (It's in Applications → Utilities.) The window shown below appears.
   
   
2. Click the AirPort Extreme's icon. The status pop-up window appears.
   
   
3. Click Edit. The settings window appears.
   
   
4. Select the Network tab. The window shown below appears.
   
   
5. Select the Enable Access Control checkbox.
   
   
6. Click Timed Access Control. The window shown below appears.
   
   
7. Select the Unlimited (default) option. By default, this allows all of the devices connected to your AirPort to access the Internet all day, every day, but you can change this to block Internet access for all devices (except the ones you specify later) during the times you set.
   
   
8. If you'd like to limit the days and times that a specific device can access the Internet, click the + button under the Wireless Clients field. The window shown below appears.
   
   
9. Enter a name for the device in the Description field.
   
   
10. Enter the device's MAC address in the MAC Address field. You can use the following tutorials to find the device's MAC address.
    
    • How to Find Your Mac's MAC Address
    • How to Find Your iPad's MAC Address
    • How to Find Your iPhone's MAC Address
    
    
11. Use the + button under the Wireless Access Times field to create a schedule for this device's Internet access.
    
    
12. Once you've added all of your devices and customized the schedules, click Save.
    
    
13. Click Update. The AirPort will restart to apply the changes.

Congratulations! You have successfully set time limits for the devices connecting to your AirPort network. The schedule you created is effective immediately.

Meet Your Macinstructor

Matt Cone, the author of Master Your Mac, has been a Mac user for over 20 years. A former ghost writer for some of Apple's most notable instructors, Cone founded Macinstruct in 1999, a site with OS X tutorials that boasts hundreds of thousands of unique visitors per month. You can email him at: matt@macinstruct.com.

In this paper we design suboptimal control laws for an unknown linear system on the basis of measured data. We focus on the suboptimal linear quadratic regulator problem and the suboptimal H2 control problem. For both problems, we establish conditions under which a given data set contains sufficient information for controller design. We follow up by providing a data-driven parameterization of all suboptimal controllers. We will illustrate our results by numerical simulations, which will reveal an interesting trade-off between the number of collected data samples and the achieved controller performance.

This work presents a novel data-driven framework for constructing eigenfunctions of the Koopman operator geared toward prediction and control. The method leverages the richness of the spectrum of the Koopman operator away from attractors to construct a rich set of eigenfunctions such that the state (or any other observable quantity of interest) is in the span of these eigenfunctions and hence predictable in a linear fashion. The eigenfunction construction is optimization-based with no dictionary selection required. Once a predictor for the uncontrolled part of the system is obtained in this way, the incorporation of control is done through a multi-step prediction error minimization, carried out by a simple linear least-squares regression. The predictor so obtained is in the form of a linear controlled dynamical system and can be readily applied within the Koopman model predictive control framework of [12] to control nonlinear dynamical systems using linear model predictive control tools. The method is entirely data-driven and based purely on convex optimization, with no reliance on neural networks or other non-convex machine learning tools. The novel eigenfunction construction method is also analyzed theoretically, proving rigorously that the family of eigenfunctions obtained is rich enough to span the space of all continuous functions. In addition, the method is extended to construct generalized eigenfunctions that also give rise Koopman invariant subspaces and hence can be used for linear prediction. Detailed numerical examples with code available online demonstrate the approach, both for prediction and feedback control.

We apply the paracontrolled calculus to study the asymptotic behavior of a certain quasilinear PDE with smeared mild noise, which originally appears as the space-time scaling limit of a particle system in random environment on one dimensional discrete lattice. We establish the convergence result and show a local in time well-posedness of the limit stochastic PDE with spatial white noise. It turns out that our limit stochastic PDE does not require any renormalization. We also show a comparison theorem for the limit equation.

Recent studies of optimization methods and GNC of spacecraft near small bodies focusing on descent, landing, rendezvous, etc., with key safety constraints such as line-of-sight conic zones and soft landings have shown promising results; this paper considers descent missions to an asteroid surface with a constraint that consists of an onboard camera and asteroid surface markers while using a stochastic convex MPC law. An undermodeled asteroid gravity and spacecraft technology inspired measurement model is established to develop the constraint. Then a computationally light stochastic Linear Quadratic MPC strategy is presented to keep the spacecraft in satisfactory field of view of the surface markers while trajectory tracking, employing chance based constraints and up-to-date estimation uncertainty from navigation. The estimation uncertainty giving rise to the tightened constraints is particularly addressed. Results suggest robust tracking performance across a variety of trajectories.

This paper addresses the problem of efficiently achieving visual predictive control tasks. To this end, a memory of motion, containing a set of trajectories built off-line, is used for leveraging precomputation and dealing with difficult visual tasks. Standard regression techniques, such as k-nearest neighbors and Gaussian process regression, are used to query the memory and provide on-line a warm-start and a way point to the control optimization process. The proposed technique allows the control scheme to achieve high performance and, at the same time, keep the computational time limited. Simulation and experimental results, carried out with a 7-axis manipulator, show the effectiveness of the approach.

Stabilizing multi-steered articulated vehicles in backward motion is a complex task for any human driver. Unless the vehicle is accurately steered, its structurally unstable joint-angle kinematics during reverse maneuvers can cause the vehicle segments to fold and enter a jack-knife state. In this work, a model predictive path-following controller is proposed enabling automatic low-speed steering control of multi-steered articulated vehicles, comprising a car-like tractor and an arbitrary number of trailers with passive or active steering. The proposed path-following controller is tailored to follow nominal paths that contains full state and control-input information, and is designed to satisfy various physical constraints on the vehicle states as well as saturations and rate limitations on the tractor's curvature and the trailer steering angles. The performance of the proposed model predictive path-following controller is evaluated in a set of simulations for a multi-steered 2-trailer with a car-like tractor where the last trailer has steerable wheels.

Robots are required to operate autonomously in response to changing situations. Previously, imitation learning using 4ch-bilateral control was demonstrated to be suitable for imitation of object manipulation. However, cooperative work between humans and robots has not yet been verified in these studies. In this study, the task was expanded by cooperative work between a human and a robot. 4ch-bilateral control was used to collect training data for training robot motion. We focused on serving salad as a task in the home. The task was executed with a spoon and a fork fixed to robots. Adjustment of force was indispensable in manipulating indefinitely shaped objects such as salad. Results confirmed the effectiveness of the proposed method as demonstrated by the success of the task.

Assistive shared-control robots have the potential to transform the lives of millions of people afflicted with severe motor impairments. The usefulness of shared-control robots typically relies on the underlying autonomy's ability to infer the user's needs and intentions, and the ability to do so unambiguously is often a limiting factor for providing appropriate assistance confidently and accurately. The contributions of this paper are four-fold. First, we introduce the idea of intent disambiguation via control mode selection, and present a mathematical formalism for the same. Second, we develop a control mode selection algorithm which selects the control mode in which the user-initiated motion helps the autonomy to maximally disambiguate user intent. Third, we present a pilot study with eight subjects to evaluate the efficacy of the disambiguation algorithm. Our results suggest that the disambiguation system (a) helps to significantly reduce task effort, as measured by number of button presses, and (b) is of greater utility for more limited control interfaces and more complex tasks. We also observe that (c) subjects demonstrated a wide range of disambiguation request behaviors, with the common thread of concentrating requests early in the execution. As our last contribution, we introduce a novel field-theoretic approach to intent inference inspired by dynamic field theory that works in tandem with the disambiguation scheme.

Wireless sensor networks have attracted much attention because of many applications in the fields of industry, military, medicine, agriculture, and education. In addition, the vast majority of researches has been done to expand its applications and improve its efficiency. However, there are still many challenges for increasing the efficiency in different parts of this network. One of the most important parts is to improve the network lifetime in the wireless sensor network. Since the sensor nodes are generally powered by batteries, the most important issue to consider in these types of networks is to reduce the power consumption of the nodes in such a way as to increase the network lifetime to an acceptable level. The contribution of this paper is using topology control, the threshold for the remaining energy in nodes, and two of the meta-algorithms include SA (Simulated annealing) and VNS (Variable Neighbourhood Search) to increase the energy remaining in the sensors. Moreover, using a low-cost spanning tree, an appropriate connectivity control among nodes is created in the network in order to increase the network lifetime. The results of simulations show that the proposed method improves the sensor lifetime and reduces the energy consumed.

By using dissipativity approach, we establish the stability condition for the feedback connection of a deterministic dynamical system $Sigma$ and a stochastic memoryless map $Psi$. After that, we extend the result to the class of large scale systems in which: $Sigma$ consists of many sub-systems; and $Psi$ consists of many "stochastic actuators" and "probability controllers" that control the actuator's output events. We will demonstrate the proposed approach by showing the design procedures to globally stabilize the manufacturing systems while locally balance the stock levels in any production process.

We show that the traveling salesman problem (TSP) and its many variants may be modeled as functional optimization problems over a graph. In this formulation, all vertices and arcs of the graph are functionals; i.e., a mapping from a space of measurable functions to the field of real numbers. Many variants of the TSP, such as those with neighborhoods, with forbidden neighborhoods, with time-windows and with profits, can all be framed under this construct. In sharp contrast to their discrete-optimization counterparts, the modeling constructs presented in this paper represent a fundamentally new domain of analysis and computation for TSPs and their variants. Beyond its apparent mathematical unification of a class of problems in graph theory, the main advantage of the new approach is that it facilitates the modeling of certain application-specific problems in their home space of measurable functions. Consequently, certain elements of economic system theory such as dynamical models and continuous-time cost/profit functionals can be directly incorporated in the new optimization problem formulation. Furthermore, subtour elimination constraints, prevalent in discrete optimization formulations, are naturally enforced through continuity requirements. The price for the new modeling framework is nonsmooth functionals. Although a number of theoretical issues remain open in the proposed mathematical framework, we demonstrate the computational viability of the new modeling constructs over a sample set of problems to illustrate the rapid production of end-to-end TSP solutions to extensively-constrained practical problems.

This paper investigates a class of optimal control problems associated with Markov processes with local state information. The decision-maker has only local access to a subset of a state vector information as often encountered in decentralized control problems in multi-agent systems. Under this information structure, part of the state vector cannot be observed. We leverage ab initio principles and find a new form of Bellman equations to characterize the optimal policies of the control problem under local information structures. The dynamic programming solutions feature a mixture of dynamics associated unobservable state components and the local state-feedback policy based on the observable local information. We further characterize the optimal local-state feedback policy using linear programming methods. To reduce the computational complexity of the optimal policy, we propose an approximate algorithm based on virtual beliefs to find a sub-optimal policy. We show the performance bounds on the sub-optimal solution and corroborate the results with numerical case studies.

In this work, we consider a group of robots working together to manipulate a rigid object to track a desired trajectory in $SE(3)$. The robots have no explicit communication network among them, and they do no know the mass or friction properties of the object, or where they are attached to the object. However, we assume they share data from a common IMU placed arbitrarily on the object. To solve this problem, we propose a decentralized adaptive control scheme wherein each agent maintains and adapts its own estimate of the object parameters in order to track a reference trajectory. We present an analysis of the controller's behavior, and show that all closed-loop signals remain bounded, and that the system trajectory will almost always (except for initial conditions on a set of measure zero) converge to the desired trajectory. We study the proposed controller's performance using numerical simulations of a manipulation task in 3D, and with hardware experiments which demonstrate our algorithm on a planar manipulation task. These studies, taken together, demonstrate the effectiveness of the proposed controller even in the presence of numerous unmodelled effects, such as discretization errors and complex frictional interactions.