By Dan Ritzman OtherWords Alaska’s Arctic National Wildlife Refuge is one of the world’s last intact ecosystems, but dangerous oil exploration could soon spoil it. I can still remember the first time I saw tracks left behind by seismic testing … Continue reading →

Earlier this week, Adobe VP and General Manager Danny Winokur disclosed that the company has concluded that HTML5 is ”the best solution for creating and deploying content in the browser across mobile platforms.” The company said it would stop building Flash to run on mobile browsers. In a blog post on the new focus of Read the rest...

Simon Scarr and Marco Hernandez at Reuters created Drowning in Plastic, visualizing the amount of plastic bottles we consume, recycle and throw away every hour, day, month, year and decade.

Around the world, almost 1 million plastic bottles are purchased every minute. As the environmental impact of that tide of plastic becomes a growing political issue, major packaged goods sellers and retailers are under pressure to cut the flow of the single-use bottles and containers that are clogging the world’s waterways.

Plastic production has surged in the last 50 years, leading to widespread use of inexpensive disposable products that are having a devastating effect on the environment. Images of plastic debris-strewn beaches and dead animals with stomachs full of plastic have sparked outrage.

Polyethylene terephthalate (PET) bottles are commonly used for soft drinks and mineral water, but can also be used in other household or personal care products. Data from Euromonitor International, shows that more than 480 billion of these bottles were sold last year alone. That’s almost 1 million every minute, as shown in the animation at the top of this page. The illustrations below show what that pile of plastic would look like if it was collected over a longer period of time.

The visuals of massive piles of plastic bottles next to recognizable landmarks helps provide context and scale to readers.

They also provided a nice Sankey Diagram showing the fate of most plastic bottles is to end up in the landfill.

Found on FlowingData

By Jessica Corbett Common Dreams “The most robust study of the ice mass balance of Antarctica to date,” scientists say, “now puts Antarctica in the frame as one of the largest contributors to sea-level rise.” Scientists are expressing alarm over … Continue reading →

For this years fishing trip to Alaska, I opted to upgrade my GoPro to a Panasonic GH-5 with Leica 12-60mm lense package at the local Best Buy. This would then accompany the Mavic drone footage. We had a couple sunny days for flying the drone, but really surprised how good the GH-5 footage turned out […]

One proves the existence and uniqueness of a generalized (mild) solution for the nonlinear Fokker--Planck equation (FPE) egin{align*} &u_t-Delta (eta(u))+{mathrm{ div}}(D(x)b(u)u)=0, quad tgeq0, xinmathbb{R}^d, d e2, \ &u(0,cdot)=u_0,mbox{in }mathbb{R}^d, end{align*} where $u_0in L^1(mathbb{R}^d)$, $etain C^2(mathbb{R})$ is a nondecreasing function, $bin C^1$, bounded, $bgeq 0$, $Din(L^2cap L^infty)(mathbb{R}^d;mathbb{R}^d)$ with ${ m div}, Din L^infty(mathbb{R}^d)$, and ${ m div},Dgeq0$, $eta$ strictly increasing, if $b$ is not constant. Moreover, $t o u(t,u_0)$ is a semigroup of contractions in $L^1(mathbb{R}^d)$, which leaves invariant the set of probability density functions in $mathbb{R}^d$. If ${ m div},Dgeq0$, $eta'(r)geq a|r|^{alpha-1}$, and $|eta(r)|leq C r^alpha$, $alphageq1,$ $alpha>frac{d-2}d$, $dgeq3$, then $|u(t)|_{L^infty}le Ct^{-frac d{d+(alpha-1)d}} |u_0|^{frac2{2+(m-1)d}},$ $t>0$, and the existence extends to initial data $u_0$ in the space $mathcal{M}_b$ of bounded measures in $mathbb{R}^d$. The solution map $mumapsto S(t)mu$, $tgeq0$, is a Lipschitz contractions on $mathcal{M}_b$ and weakly continuous in $tin[0,infty)$. As a consequence for arbitrary initial laws, we obtain weak solutions to a class of McKean-Vlasov SDEs with coefficients which have singular dependence on the time marginal laws.

In this paper, we mainly establish the existence and uniqueness theorem for solutions of the exterior Dirichlet problem for a class of fully nonlinear second-order elliptic equations related to the eigenvalues of the Hessian, with prescribed generalized symmetric asymptotic behavior at infinity. Moreover, we give some new results for the Hessian equations, Hessian quotient equations and the special Lagrangian equations, which have been studied previously.

In this article, we prove that the completeness of the Hamilton flow and essential self-dajointness are equivalent for real principal type operators on the circle. Moreover, we study spectral properties of these operators.

We show weak-strong uniqueness and stability results for the motion of a two or three dimensional fluid governed by the Navier-Stokes equation interacting with a flexible, elastic plate of Koiter type. The plate is situated at the top of the fluid and as such determines the variable part of a time changing domain (that is hence a part of the solution) containing the fluid. The uniqueness result is a consequence of a stability estimate where the difference of two solutions is estimated by the distance of the initial values and outer forces. For that we introduce a methodology that overcomes the problem that the two (variable in time) domains of the fluid velocities and pressures are not the same. The estimate holds under the assumption that one of the two weak solutions possesses some additional higher regularity. The additional regularity is exclusively requested for the velocity of one of the solutions resembling the celebrated Ladyzhenskaya-Prodi-Serrin conditions in the framework of variable domains.

Let $k ,=, mathbb{Q}(sqrt[5]{n},zeta_5)$, where $n$ is a positive integer, $5^{th}$ power-free, whose $5-$class group is isomorphic to $mathbb{Z}/5mathbb{Z} imesmathbb{Z}/5mathbb{Z}$. Let $k_0,=,mathbb{Q}(zeta_5)$ be the cyclotomic field containing a primitive $5^{th}$ root of unity $zeta_5$. Let $C_{k,5}^{(sigma)}$ the group of the ambiguous classes under the action of $Gal(k/k_0)$ = $<sigma>$. The aim of this paper is to determine all integers $n$ such that the group of ambiguous classes $C_{k,5}^{(sigma)}$ has rank $1$ or $2$.

We investigate quasistatic evolution in finite plasticity under the assumption that the plastic strain is compatible. This assumption is well-suited to describe the special case of dislocation-free plasticity and entails that the plastic strain is the gradient of a plastic deformation map. The total deformation can be then seen as the composition of a plastic and an elastic deformation. This opens the way to an existence theory for the quasistatic evolution problem featuring both Lagrangian and Eulerian variables. A remarkable trait of the result is that it does not require second-order gradients.

We study flows on C*-algebras with the Rokhlin property. We show that every Kirchberg algebra carries a unique Rokhlin flow up to cocycle conjugacy, which confirms a long-standing conjecture of Kishimoto. We moreover present a classification theory for Rokhlin flows on C*-algebras satisfying certain technical properties, which hold for many C*-algebras covered by the Elliott program. As a consequence, we obtain the following further classification theorems for Rokhlin flows. Firstly, we extend the statement of Kishimoto's conjecture to the non-simple case: Up to cocycle conjugacy, a Rokhlin flow on a separable, nuclear, strongly purely infinite C*-algebra is uniquely determined by its induced action on the prime ideal space. Secondly, we give a complete classification of Rokhlin flows on simple classifiable $KK$-contractible C*-algebras: Two Rokhlin flows on such a C*-algebra are cocycle conjugate if and only if their induced actions on the cone of lower-semicontinuous traces are affinely conjugate.

We study a class of fourth order nonlinear parabolic equations which include the thin-film equation and the quantum drift-diffusion model as special cases. We investigate these equations by first developing functional inequalities of the type $ int_Omega u^{2gamma-alpha-eta}Delta u^alphaDelta u^eta dx geq cint_Omega|Delta u^gamma |^2dx $, which seem to be of interest on their own right.

We prove small data modified scattering for the Vlasov-Poisson system in dimension $d=3$ using a method inspired from dispersive analysis. In particular, we identify a simple asymptotic dynamic related to the scattering mass.

In the spirit of very recent articles by J. Bonet, W. Lusky and J. Taskinen we are studying the so-called solid hulls and cores of spaces of weighted entire functions when the weights are given in terms of associated weight functions coming from weight sequences. These sequences are required to satisfy certain (standard) growth and regularity properties which are frequently arising and used in the theory of ultradifferentiable and ultraholomorphic function classes (where also the associated weight function plays a prominent role). Thanks to this additional information we are able to see which growth behavior the so-called "Lusky-numbers", arising in the representations of the solid hulls and cores, have to satisfy resp. if such numbers can exist.

We establish the functional convex order results for two scaled McKean-Vlasov processes $X=(X_{t})_{tin[0, T]}$ and $Y=(Y_{t})_{tin[0, T]}$ defined by

[egin{cases} dX_{t}=(alpha X_{t}+eta)dt+sigma(t, X_{t}, mu_{t})dB_{t}, quad X_{0}in L^{p}(mathbb{P}),\ dY_{t}=(alpha Y_{t},+eta)dt+ heta(t, Y_{t}, u_{t})dB_{t}, quad Y_{0}in L^{p}(mathbb{P}). end{cases}] If we make the convexity and monotony assumption (only) on $sigma$ and if $sigmaleq heta$ with respect to the partial matrix order, the convex order for the initial random variable $X_0 leq Y_0$ can be propagated to the whole path of process $X$ and $Y$. That is, if we consider a convex functional $F$ with polynomial growth defined on the path space, we have $mathbb{E}F(X)leqmathbb{E}F(Y)$; for a convex functional $G$ defined on the product space involving the path space and its marginal distribution space, we have $mathbb{E},Gig(X, (mu_t)_{tin[0, T]}ig)leq mathbb{E},Gig(Y, ( u_t)_{tin[0, T]}ig)$ under appropriate conditions. The symmetric setting is also valid, that is, if $ heta leq sigma$ and $Y_0 leq X_0$ with respect to the convex order, then $mathbb{E},F(Y) leq mathbb{E},F(X)$ and $mathbb{E},Gig(Y, ( u_t)_{tin[0, T]}ig)leq mathbb{E},G(X, (mu_t)_{tin[0, T]})$. The proof is based on several forward and backward dynamic programming and the convergence of the Euler scheme of the McKean-Vlasov equation.

Let $H$ be an infinite dimensional, reflexive, separable Hilbert space and $NA(H)$ the class of all norm-attainble operators on $H.$ In this note, we study an implicit scheme for a canonical representation of nonexpansive contractions in norm-attainable classes.

We introduce natural deformation classes of generalized K"ahler structures using the Courant symmetry group. We show that these yield natural extensions of the notions of K"ahler class and K"ahler cone to generalized K"ahler geometry. Lastly we show that the generalized K"ahler-Ricci flow preserves this generalized K"ahler cone, and the underlying real Poisson tensor.

High-performance implementations of graph algorithms are challenging to implement on new parallel hardware such as GPUs, because of three challenges: (1) difficulty of coming up with graph building blocks, (2) load imbalance on parallel hardware, and (3) graph problems having low arithmetic intensity. To address these challenges, GraphBLAS is an innovative, on-going effort by the graph analytics community to propose building blocks based in sparse linear algebra, which will allow graph algorithms to be expressed in a performant, succinct, composable and portable manner. In this paper, we examine the performance challenges of a linear algebra-based approach to building graph frameworks and describe new design principles for overcoming these bottlenecks. Among the new design principles is exploiting input sparsity, which allows users to write graph algorithms without specifying push and pull direction. Exploiting output sparsity allows users to tell the backend which values of the output in a single vectorized computation they do not want computed. Load-balancing is an important feature for balancing work amongst parallel workers. We describe the important load-balancing features for handling graphs with different characteristics. The design principles described in this paper have been implemented in "GraphBLAST", the first open-source linear algebra-based graph framework on GPU targeting high-performance computing. The results show that on a single GPU, GraphBLAST has on average at least an order of magnitude speedup over previous GraphBLAS implementations SuiteSparse and GBTL, comparable performance to the fastest GPU hardwired primitives and shared-memory graph frameworks Ligra and Gunrock, and better performance than any other GPU graph framework, while offering a simpler and more concise programming model.

A recently introduced classifier, called SS3, has shown to be well suited to deal with early risk detection (ERD) problems on text streams. It obtained state-of-the-art performance on early depression and anorexia detection on Reddit in the CLEF's eRisk open tasks. SS3 was created to deal with ERD problems naturally since: it supports incremental training and classification over text streams, and it can visually explain its rationale. However, SS3 processes the input using a bag-of-word model lacking the ability to recognize important word sequences. This aspect could negatively affect the classification performance and also reduces the descriptiveness of visual explanations. In the standard document classification field, it is very common to use word n-grams to try to overcome some of these limitations. Unfortunately, when working with text streams, using n-grams is not trivial since the system must learn and recognize which n-grams are important "on the fly". This paper introduces t-SS3, an extension of SS3 that allows it to recognize useful patterns over text streams dynamically. We evaluated our model in the eRisk 2017 and 2018 tasks on early depression and anorexia detection. Experimental results suggest that t-SS3 is able to improve both current results and the richness of visual explanations.

Traffic signs are essential map features globally in the era of autonomous driving and smart cities. To develop accurate and robust algorithms for traffic sign detection and classification, a large-scale and diverse benchmark dataset is required. In this paper, we introduce a traffic sign benchmark dataset of 100K street-level images around the world that encapsulates diverse scenes, wide coverage of geographical locations, and varying weather and lighting conditions and covers more than 300 manually annotated traffic sign classes. The dataset includes 52K images that are fully annotated and 48K images that are partially annotated. This is the largest and the most diverse traffic sign dataset consisting of images from all over world with fine-grained annotations of traffic sign classes. We have run extensive experiments to establish strong baselines for both the detection and the classification tasks. In addition, we have verified that the diversity of this dataset enables effective transfer learning for existing large-scale benchmark datasets on traffic sign detection and classification. The dataset is freely available for academic research: https://www.mapillary.com/dataset/trafficsign.

We present results showing the capability of concrete-based information processing substrate in the signal classification task in accordance with in materio computing paradigm. As the Reservoir Computing is a suitable model for describing embedded in materio computation, we propose that this type of presented basic construction unit can be used as a source for "reservoir of states" necessary for simple tuning of the readout layer. In that perspective, buildings constructed from computing concrete could function as a highly parallel information processor for smart architecture. We present an electrical characterization of the set of samples with different additive concentrations followed by a dynamical analysis of selected specimens showing fingerprints of memfractive properties. Moreover, on the basis of obtained parameters, classification of the signal waveform shapes can be performed in scenarios explicitly tuned for a given device terminal.

This paper proposes a fully automated atlas-based pancreas segmentation method from CT volumes utilizing atlas localization by regression forest and atlas generation using blood vessel information. Previous probabilistic atlas-based pancreas segmentation methods cannot deal with spatial variations that are commonly found in the pancreas well. Also, shape variations are not represented by an averaged atlas. We propose a fully automated pancreas segmentation method that deals with two types of variations mentioned above. The position and size of the pancreas is estimated using a regression forest technique. After localization, a patient-specific probabilistic atlas is generated based on a new image similarity that reflects the blood vessel position and direction information around the pancreas. We segment it using the EM algorithm with the atlas as prior followed by the graph-cut. In evaluation results using 147 CT volumes, the Jaccard index and the Dice overlap of the proposed method were 62.1% and 75.1%, respectively. Although we automated all of the segmentation processes, segmentation results were superior to the other state-of-the-art methods in the Dice overlap.

Convolutional Neural Networks (CNNs) are generally prone to noise interruptions, i.e., small image noise can cause drastic changes in the output. To suppress the noise effect to the final predication, we enhance CNNs by replacing max-pooling, strided-convolution, and average-pooling with Discrete Wavelet Transform (DWT). We present general DWT and Inverse DWT (IDWT) layers applicable to various wavelets like Haar, Daubechies, and Cohen, etc., and design wavelet integrated CNNs (WaveCNets) using these layers for image classification. In WaveCNets, feature maps are decomposed into the low-frequency and high-frequency components during the down-sampling. The low-frequency component stores main information including the basic object structures, which is transmitted into the subsequent layers to extract robust high-level features. The high-frequency components, containing most of the data noise, are dropped during inference to improve the noise-robustness of the WaveCNets. Our experimental results on ImageNet and ImageNet-C (the noisy version of ImageNet) show that WaveCNets, the wavelet integrated versions of VGG, ResNets, and DenseNet, achieve higher accuracy and better noise-robustness than their vanilla versions.

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE) and AUC achieved by our method are 91.79%, 93.05%, 89.95% and 96.35%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.

Water quality has a direct impact on industry, agriculture, and public health. Algae species are common indicators of water quality. It is because algal communities are sensitive to changes in their habitats, giving valuable knowledge on variations in water quality. However, water quality analysis requires professional inspection of algal detection and classification under microscopes, which is very time-consuming and tedious. In this paper, we propose a novel multi-target deep learning framework for algal detection and classification. Extensive experiments were carried out on a large-scale colored microscopic algal dataset. Experimental results demonstrate that the proposed method leads to the promising performance on algal detection, class identification and genus identification.

We establish versions of Conley's (i) fundamental theorem and (ii) decomposition theorem for a broad class of hybrid dynamical systems. The hybrid version of (i) asserts that a globally-defined "hybrid complete Lyapunov function" exists for every hybrid system in this class. Motivated by mechanics and control settings where physical or engineered events cause abrupt changes in a system's governing dynamics, our results apply to a large class of Lagrangian hybrid systems (with impacts) studied extensively in the robotics literature. Viewed formally, these results generalize those of Conley and Franks for continuous-time and discrete-time dynamical systems, respectively, on metric spaces. However, we furnish specific examples illustrating how our statement of sufficient conditions represents merely an early step in the longer project of establishing what formal assumptions can and cannot endow hybrid systems models with the topologically well characterized partitions of limit behavior that make Conley's theory so valuable in those classical settings.

Monty Spears didn't know it at the time, but the last time he'd see his father would be through the window at the Life Care Center of Lewiston.…

We could blame the enormous — and justifiable — success of the Lego flicks for the existence of Playmobil: The Movie, but that would be unfair to all the shameless knockoffs and cinematic coattail riders.…

What am I supposed to say here?…

You might not know it from the fancy attire they wear on stage at the Fox Theater, but for the musicians in the Spokane Symphony, it's a part-time gig. It's a prestigious gig, to be sure, but like most artists, for the musicians, it's just one piece of a puzzle full of hustle they have to solve to make a living.…

Here are a few ways to keep yourself entertained, and maybe even educate yourself a bit, while you're stuck at home:…

THE SHOW WILL GO ON…

Techniques for evaluation and/or retraining of a classification model built using labeled training data. In some aspects, a classification model having a first set of weights is retrained by using unlabeled input to reweight the labeled training data to have a second set of weights, and by retraining the classification model using the labeled training data weighted according to the second set of weights. In some aspects, a classification model is evaluated by building a similarity model that represents similarities between unlabeled input and the labeled training data and using the similarity model to evaluate the labeled training data to identify a subset of the plurality of items of labeled training data that is more similar to the unlabeled input than a remainder of the labeled training data.

A system and method for classifying unclassified samples. The method includes detecting a number of classes including training samples in training data sets. The method includes, for each class, determining a vector for each training sample based on a specified number of nearest neighbor distances between the training sample and neighbor training samples, and determining a class distribution based on the vectors. The method also includes detecting an unclassified sample in a data set and, for each class, determining a vector for the unclassified sample based on the specified number of nearest neighbor distances between the unclassified sample and nearest neighbor training samples within the class, and determining a probability that the unclassified sample is a member of the class based on the vector and the class distribution. The method further includes classifying the unclassified sample based on the probabilities.

A system and method for providing more than two levels of classification distinction of a user state are provided. The first and second general states of a user are sensed. The first general state is classified as either a first state or a second state, and the second general state is classified as either a third state or a fourth state. The user state of the user is then classified as one of at least three different classification states.

A computer-implemented method for classifying documents for data loss prevention may include 1) identifying training documents for a machine learning classifier configured for data loss prevention, 2) performing a semantic analysis on training documents to identify topics within the set training documents, 3) applying a similarity metric to the topics to identify at least one unrelated topic with a similarity to the other topics within the plurality of topics, as determined by the similarity metric, that falls below a similarity threshold, 4) identifying, based on the semantic analysis, at least one irrelevant training document within the set of training documents in which a predominance of the unrelated topic is above a predominance threshold, and 5) excluding the irrelevant training document from the set of training documents based on the predominance of the unrelated topic within the irrelevant training document. Various other methods, systems, and computer-readable media are also disclosed.

A computer implemented method for automatic chemical assay classification, the method comprising steps the computer is programmed to perform, the steps comprising: receiving a plurality of sets of parameters, each one of the received sets of parameters characterizing a respective assay of a chemical reaction, calculating a space enhancing proximity among points representative of assays of qualitatively identical chemical reactions, and representing each one of at least two of the received sets of parameters as a respective point in the calculated space, and dividing the points in the calculated space into a number of groups, according to proximity among the points in the calculated space, each group pertaining to a respective chemical reaction, thereby classifying the assays.

A method for preparing 13C labeled plasmalogens as represented by Formula B: The method involves producing a 13C labeled cyclic plasmalogen precursor of Formula A: and conversion of the precursor to a plasmalogen of Formula B.

Formaldehyde-free binder compositions are described that include an aldehyde or ketone, a nitrogen-containing salt of an inorganic acid, and an acidic compound. The acidic compound may be an organic acid, such as maleic acid or citric acid among others. The acidic compound is supplied in quantities that lower the pH of the binder composition to about 5 or less. The binder compositions may be used in methods of binding fiberglass and the resulting fiberglass products have an improved tensile strength due to the addition of the acidic compound.

The object of the present invention is to provide an intermediate film for laminated glass which prevents reductions of transparency and appearance property caused by permeation of moisture and water. The above object is attained by the present invention, i.e., a composition for forming an intermediate film for a laminated glass which comprises ethylene-vinyl acetate copolymer and a compound having alkyleneoxy group. The composition of the present invention enables the formation of an intermediate film for a laminated glass that restrains white spots from occurring by the permeation of moisture and water.

An ion-conductive thermoplastic compositions contains a partially acetalated polyvinyl alcohol, at least one support electrolyte and at least one plasticizer. The partially acetalated polyvinyl alcohol contains two different acetal units. Electrochromic laminated glass systems produced using the ion-conductive compositions and a method for producing the systems are also provided.

Plasticizing system which can be used in particular for the plasticizing of a tire diene elastomer composition containing, in combination: a plasticizing hydrocarbon resin, the glass transition temperature of which is greater than 0° C.; anda tri- or pyromellitate ester corresponding to the formula (I): in which the R radicals, which are identical or different, represent a hydrocarbon radical and the R' radical represents hydrogen or COOR group. Also, a rubber composition incorporating the plasticizing system and the use of such a composition for the manufacture of a tire or tire semi-finished product made of rubber, in particular of a tire tread, exhibiting an improved wear resistance without having a detrimental affect on its wet grip.

The invention relates to glasses for use in solid laser applications, particularly short-pulsed, high peak power laser applications. In particular, the invention relates to a method for broadening the emission bandwidth of rare earth ions used as lasing ions in solid laser glass mediums, especially phosphate-based glass compositions, using Nd and Yb as co-dopants. The invention further relates to a laser system using a Nd-doped and Yb-doped phosphate laser glass, and a method of generating a laser beam pulse using such a laser system.

Disclosed are a vacuum glass panel and a manufacturing method of the same. The vacuum glass panel according to the present invention includes a first glass plate, a second glass plate facing the first glass plate with a vacuum space therebetween, an edge of the second glass plate being in contact with the first glass plate, and a plurality of spacers disposed between the first glass plate and the second glass plates to separate the first glass plate and the second glass plate from each other. The plurality of spacers are formed of a glass including alumina (Al2O3) particles and silica (SiO2) particles.

Embodiments are directed to glass frits containing phosphors that can be used in LED lighting devices and for methods associated therewith for making the phosphor containing glass frit and their use in glass articles, for example, LED devices.

There is provided a glass for chemical strengthening having a black color tone and excelling in characteristics preferred for the purposes of housing or decoration of an electronic device, that is, bubble quality, strength, and light transmittance characteristics. A glass for chemical strengthening contains, in mole percentage based on following oxides, 55% to 80% of SiO2, 3% to 16% of Al2O3, 0% to 12% of B2O3, 5% to 16% of Na2O, 0% to 4% of K2O, 0% to 15% of MgO, 0% to 3% of CaO, 0% to 18% of ΣRO (where R represents Mg, Ca, Sr, Ba or Zn), 0% to 1% of ZrO2, and 0.1% to 7% of a coloring component having at least one metal oxide selected from the group consisting of oxides of Co, Mn, Fe, Ni, Cu, Cr, V and Bi.

A method for manufacturing a plurality of glass microspheres comprises: melting a batch into a first glass melt in a melter system, processing the first glass melt into a second glass, pulverizing the second glass into a plurality of glass fragments, thermally processing the plurality of glass fragments into a plurality of glass microspheres, providing at least one of a plurality of redox reactions and a plurality of events in at least one of the first glass melt and a melt of the second glass, and the plurality of redox reactions and the plurality of events are induced by a plurality of redox active group (RAG) components.

A glass for a magnetic recording medium substrate permitting the realization of a magnetic recording medium substrate affording good chemical durability and having an extremely flat surface, a magnetic recording medium substrate comprised of this glass, a magnetic recording medium equipped with this substrate, and methods of manufacturing the same. The glass is an oxide glass not including As or F.

A glass ceramic as cooktop for induction heating having improved colored display capability and heat shielding is provided. The cooktop includes a transparent, dyed glass ceramic plate having high-quartz mixed crystals as a predominant crystal phase. The glass ceramic contains none of the chemical refining agents arsenic oxide and/or antimony oxide and has a transmittance values greater than 0.4% at at least one wavelength in the blue spectrum between 380 and 500 nm, a transmittance >2% at 630 nm, a transmittance of less than 45% at 1600 nm, and a light transmittance of less than 2.5% in the visible spectrum.