On 29 July 2019, we published an article originally headlined "Islamic Caliphate in rural England: 35000 to pledge allegiance in mass conversion ceremony".

Premier League quiz questions are a great way of testing your top-flight knowledge with friends and family.

Yesterday, Prime Minister Boris Johnson told an international video conference that we face a battle of "humanity against the virus". How right that is - and the battle has started. According to Professor Nicholas Hart, one of the doctors who saved Mr Johnson's life, "COVID-19 is this generation's polio."

Joe Young has been playing in China since his time with the Indiana Pacers ended. He is a Houston native

Indiana educators are among the strongest advocates for students and deserve the utmost appreciation and respect, Jennifer McCormick writes.

The US-China over artificial intelligence is heating up, but some warn the US could be over-reacting.

The rapper was arrested earlier this month after an altercation in Stockholm.

Celebrity Edge is the third class, or type, of ship in the Celebrity Cruises Fleet. The first ship was Celebrity Edge in 2018, followed by Celebrity Apex in 2020. After spending time on the ship, and talking to cruise passengers, I review this new class of ship and discuss what is new and different, and what is good or poor about the ship - and who I think it is best for. This should help you decide if the Celebrity Edge ships are right for your cruising vacation.

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Teacher question: You wrote recently that it was a good idea to teach comprehension skills, but our school district says we shouldn’t, that it’s prior knowledge that matters. Do you know the baseball study? Have you read Natalie Wexler’s research? It is really difficult to trust research when everyone tells us something different. Shanahan's response:  I feel your pain.

Teacher question: You wrote recently that it was a good idea to teach comprehension skills, but our school district says we shouldn’t, that it’s prior knowledge that matters. Do you know the baseball study? Have you read Natalie Wexler’s research? It is really difficult to trust research when everyone tells us something different. Shanahan's response:  I feel your pain.

Rita J. King, co-director and EVP for business development at Science House, recently conducted a series of interviews with business leaders, exploring the challenges and hurdles companies face in evolving business landscapes. In this interview, King chats with Craig Lemasters, CEO of Global Executive Group, about what companies face when navigating the digital transformation. They […]

8 January 2020 , Volume 96, Number 1

8 January 2020 , Volume 96, Number 1

Cutters of diamonds and other gemstones have a high-pressure job with conflicting demands: Flaws must be removed from rough stones to maximize brilliance but done so in a way that yields the greatest weight possible. Because diamonds are often cut to a standard shape, cutting them is far less complex than cutting other gemstones, such as rubies or sapphires, which can have hundreds of different shapes. By coupling geometry and multivariable calculus with optimization techniques, mathematicians have been able to devise algorithms that automatically generate precise cutting plans that maximize brilliance and yield. The goal is to find the final shape within a rough stone. There are an endless number of candidates, positions, and orientations, so finding the shape amounts to a maximization problem with a large number of variables subject to an infinite number of constraints, a technique called semi-infinite optimization. Experienced human cutters create finished gems that average about 1/3 of the weight of the original rough stone. Cutting with this automated algorithm improved the yield to well above 40%, which, given the value of the stones, is a tremendous improvement. Without a doubt, semi-infinite optimization is a girl.s (or boy.s) best friend.

(University of Massachusetts Lowell) A UMass Lowell researcher investigating how to identify damage in wind turbines before they fail has received $1.4 million to develop a solution.

(Aarhus University) An international team of researchers has put together a new image of Neanderthals based on the genes Neanderthals left in the DNA of modern humans when they had children with them about 50,000 years ago. The researchers found the new information by trawling the genomes of more than 27,000 Icelanders. Among other things, they discovered that Neanderthal children had older mothers and younger fathers than the Homo-Sapien children in Africa did at the time.

Rachel L. Derr
Jul 1, 2007; 20:177-185
Feature Articles

Diabetic Keratopathy, a sight-threatening corneal disease, comprises several symptomatic conditions including delayed epithelial wound healing, recurrent erosions, and sensory nerve (SN) neuropathy. We investigated the role of neuropeptides in mediating corneal wound healing, including epithelial wound closure and SN regeneration. Denervation by Resiniferatoxin severely impaired corneal wound healing and markedly up-regulated pro-inflammatory gene expression. Exogenous neuropeptides CGRP, SP, and VIP partially reversed Resiniferatoxin’s effects, with VIP specifically inducing IL-10 expression. Hence, we focused on VIP and observed that wounding induced VIP and VIPR1 expression in normal (NL), but not diabetic (DM) mouse corneas. Targeting VIPR1 in NL corneas attenuated corneal wound healing, dampened wound-induced expression of neurotrophic factors, and exacerbated inflammatory responses while exogenous VIP had the opposite effects in DM corneas. Remarkably, wounding and diabetes also affected the expression of Sonic Hedgehog (SHH) in a VIP-dependent manner. Downregulating SHH expression in NL corneas decreased, while exogenous SHH in DM corneas increased the rates of corneal wound healing. Furthermore, inhibition of SHH signaling dampened VIP-promoted corneal wound healing. We conclude that VIP regulates epithelial wound healing, inflammatory response, and nerve regeneration in the corneas in a SHH-dependent manner, suggesting a therapeutic potential for these molecules in treating diabetic keratopathy.

In every generation there are a few that know the secret; the counterintuitive effects of loop diuretics. In this podcast Steven Anisman, cardiologist at the Dartmouth Hitchcock Department of Cardiovascular Medicine, joins us to explain about the threshold effects of these drugs, and why that might change the way in which you think about...

In its first year, the Trump administration moved to deliver on some of Donald Trump’s campaign promises on immigration, including ramping up enforcement in the U.S. interior and ending the Deferred Action for Childhood Arrivals (DACA) program. The administration also announced the termination of Temporary Protected Status (TPS) for nationals of some countries. This article explores some of the top policy changes.

Pledge to celebrate World Oral Health Day on March 20 with the rest of the sphere.

To tackle a problem as large as climate change, we need both science and Indigenous wisdom, says environmental activist Hindou Oumarou Ibrahim. In this engaging talk, she shares how her nomadic community in Chad is working closely with scientists to restore endangered ecosystems -- and offers lessons on how to create more resilient communities.

The Knowledge Sharing for the Development of Learning Resources tutorial provides a professional step forward, a learning experience that leads to recognition that your leadership is well founded as well as ensuring that participants in the development of learning resources recognize they are contributing to an exceptional achievement.

Ability -- United States.

London : Smith, Elder, 1887.

Springfield, Virginia : National Technical Information Service, 1985.

Latent space models are effective tools for statistical modeling and visualization of network data. Due to their close connection to generalized linear models, it is also natural to incorporate covariate information in them. The current paper presents two universal fitting algorithms for networks with edge covariates: one based on nuclear norm penalization and the other based on projected gradient descent. Both algorithms are motivated by maximizing the likelihood function for an existing class of inner-product models, and we establish their statistical rates of convergence for these models. In addition, the theory informs us that both methods work simultaneously for a wide range of different latent space models that allow latent positions to affect edge formation in flexible ways, such as distance models. Furthermore, the effectiveness of the methods is demonstrated on a number of real world network data sets for different statistical tasks, including community detection with and without edge covariates, and network assisted learning.

In recent years, multi-access edge computing (MEC) is a key enabler for handling the massive expansion of Internet of Things (IoT) applications and services. However, energy consumption of a MEC network depends on volatile tasks that induces risk for energy demand estimations. As an energy supplier, a microgrid can facilitate seamless energy supply. However, the risk associated with energy supply is also increased due to unpredictable energy generation from renewable and non-renewable sources. Especially, the risk of energy shortfall is involved with uncertainties in both energy consumption and generation. In this paper, we study a risk-aware energy scheduling problem for a microgrid-powered MEC network. First, we formulate an optimization problem considering the conditional value-at-risk (CVaR) measurement for both energy consumption and generation, where the objective is to minimize the loss of energy shortfall of the MEC networks and we show this problem is an NP-hard problem. Second, we analyze our formulated problem using a multi-agent stochastic game that ensures the joint policy Nash equilibrium, and show the convergence of the proposed model. Third, we derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based asynchronous advantage actor-critic (A3C) algorithm with shared neural networks. This method mitigates the curse of dimensionality of the state space and chooses the best policy among the agents for the proposed problem. Finally, the experimental results establish a significant performance gain by considering CVaR for high accuracy energy scheduling of the proposed model than both the single and random agent models.

Increasing number of sectors which affect human lives, are using Machine Learning (ML) tools. Hence the need for understanding their working mechanism and evaluating their fairness in decision-making, are becoming paramount, ushering in the era of Explainable AI (XAI). In this contribution we introduced a few intrinsically interpretable models which are also capable of dealing with missing values, in addition to extracting knowledge from the dataset and about the problem. These models are also capable of visualisation of the classifier and decision boundaries: they are the angle based variants of Learning Vector Quantization. We have demonstrated the algorithms on a synthetic dataset and a real-world one (heart disease dataset from the UCI repository). The newly developed classifiers helped in investigating the complexities of the UCI dataset as a multiclass problem. The performance of the developed classifiers were comparable to those reported in literature for this dataset, with additional value of interpretability, when the dataset was treated as a binary class problem.