Miracle-dz/UniFAR-FTUs huggingface dataset:
Models Used to Predict Chemical Bioaccumulation in Fish from in Vitro Biotransformation Rates Require Accurate Estimates of Blood–Water Partitioning and Chemical Volume of Distribution

Methods for extrapolating measured in vitro intrinsic clearance to a whole‐body biotransformation rate constant (kB) have been developed to support modeled bioaccumulation assessments for fish. The inclusion of extrapolated kB values into existing bioaccumulation models improves the prediction of chemical bioconcentration factors (BCFs), but there remains a tendency for these methods to overestimate BCFs relative to measured values. Therefore, a need exists to evaluate the extrapolation procedure to assess potential sources of error in predicted kB values. We examined how three different approaches (empirically based, composition based, and polyparameter linear free energy relationships [ppLFERs]) used to predict chemical partitioning in vitro (liver S9 system; KS9W), in blood (KBW), and in whole fish tissues (KFW) impact the prediction of a chemical's hepatic clearance binding term (fU) and apparent volume of distribution (VD), both of which factor into the calculation of kB and the BCF. Each approach yielded different KS9W, KBW, and KFW values, but resulted in fU values that were of similar magnitude and remained relatively constant at log octanol–water partition ratios (KOW) greater than 4. This is because KBW and KS9W values predicted by any given approach exhibit a similar dependence on log KOW (i.e., regression slope), which results in a cancelation of “errors” when fU is calculated. In contrast, differences in KBW values predicted by the three approaches translate to differences in VD, and by extension kB and the BCF, which become most apparent at log KOW greater than 6. There is a need to collect KBW and VD data for hydrophobic chemicals in fish that can be used to evaluate and improve existing partitioning prediction approaches in extrapolation models for fish. Environ Toxicol Chem 2023;42:33–45. © 2022 SETAC
Differential Geometry of Curves and Surfaces

Plane Curves: Local Properties Parametrizations Position, Velocity, and Acceleration Curvature Osculating Circles, Evolutes, and Involutes Natural Equations Plane Curves: Global Properties Basic Properties Rotation Index Isoperimetric Inequality Curvature, Convexity, and the Four-Vertex Theorem Curves in Space: Local Properties Definitions, Examples, and Differentiation Curvature, Torsion, and the Frenet Frame Osculating Plane and Osculating Sphere Natural Equations Curves in Space: Global Properties Basic Properties Indicatrices and Total Curvature Knots and Links Regular Surfaces Parametrized Surfaces Tangent Planes and Regular Surfaces Change of Coordinates The Tangent Space and the Normal Vector Orientable Surfaces The First and Second Fundamental Forms The First Fundamental Form Map Projections (Optional) The Gauss Map The Second Fundamental Form Normal and Principal Curvatures Gaussian and Mean Curvature Developable Surfaces and Minimal Surfaces The Fundamental Equations of Surfaces Gauss's Equations and the Christoffel Symbols Codazzi Equations and the Theorema Egregium The Fundamental Theorem of Surface Theory The Gauss-Bonnet Theorem and Geometry of Geodesics Curvatures and Torsion Gauss-Bonnet Theorem, Local Form Gauss-Bonnet Theorem, Global Form Geodesics Geodesic Coordinates Applications to Plane, Spherical and Elliptic Geometry Hyperbolic Geometry Curves and Surfaces in n-Dimensional Euclidean Space Curves in n-Dimensional Euclidean Space Surfaces in Rn Appendix: Tensor Notation
Design and Evaluation of Matrix Tablets of Ambroxol Hydrochloride using Guargum

The objective of the study was to develop guargum matrix tablets for oral controlled release of Ambroxol hydrochloride. Matrix tablets of Ambroxol hydrochloride, using various viscosity grades of guar gum in three proportions, were prepared by wet granulation method. The granules were evaluated for angle of repose, bulk density, compressibility index, total porosity and drug content. The tablets were subjected to thickness, weight variation test, drug content, hardness, friability and in vitro release studies. The granules showed satisfactory flow properties, compressibility and drug content. All the tablet formulations showed acceptable pharmacotechnical properties and complied with in-house specifications for tested parameters. According to the theoretical release profile calculation, a twice daily sustainedrelease formulation should release 19.6 mg of ambroxol hydrochloride in 1 hour like conventional tablets, and 5.2 mg per hour upto 12 hours. Ambroxol hydrochloride matrix tablets containing either 30%wt/wt of low viscosity (F-III), 25% wt/wt medium viscosity (F-VI) or 20%wt/wt high viscosity (F-IX) guar gum showed sustained release. The results of dissolution studies indicated that formulation F-IX is the most successful formulation of the study and exhibited satisfactory drug release in the initial hours and the total release pattern was very close to the theoretical release profile as well as marketed sustained release ambroxol hydrochloride tablets. Applying exponential equation, the selected formulations F-III and F-VI showed diffusion-dominated drug release and followed first order kinetics.The mechanism of drug release from F-IX was diffusion coupled with erosion. Guar gum matrix tablets F-IX showed no change in physical appearance, drug content, or in dissolution pattern after storage at 40°C/relative humidity 75% for 6 months.
Simple analytical model of vortex-lattice melting in two-dimensional superconductors

The melting of the Abrikosov vortex lattice in a 2D type-II superconductor at high magnetic fields is studied analytically within the framework of the phenomenological Ginzburg-Landau theory. It is shown that local phase fluctuations in the superconducting order parameter, associated with low energies sliding motions of Bragg chains along the principal crystallographic axes of the vortex lattice, lead to a weak first order 'melting' transition at a certain temperature $T_{m}$, well below the mean field $T_{c\text{}}$, where the shear modulus drops abruptly to a nonzero value. The residual shear modulus above $T_{m}$ decreases asymptotically to zero with increasing temperature. Despite the large phase fluctuations, the average positions of Bragg chains at fimite temperature correspond to a regular vortex lattice, slightly distorted with respect to the triangular Abrikosov lattice. It is also shown that a genuine long range phase coherence exists only at zero temperature; however, below the melting point the vortex state is very close to the triangular Abrikosov lattice. A study of the size dependence of the structure factor at finite temperature indicates the existence of quasi-long range order with $S(\overrightarrow{G}) \sim N^{\sigma}$, and $1/2<\sigma <1$, where superconducting crystallites of correlated Bragg chains grow only along pinning chains. This finding may suggest a very efficient way of generating pinning defects in quasi 2D superconductors. Our results for the melting temperature and for the entropy jump agree with the state of the art Monte Carlo simulations.
Reduktion des Kommunikationsaufwands iterierter Runge-Kutta-Verfahren für dünnbesetzte gewöhnliche Differentialgleichungssysteme

Iterated Runge-Kutta (IRK) methods are a class of solution methods for initial value problems of systems of ordinary differential equations (ODE) which offer a high level of parallelization. While implementations for dense systems of ODE have to exchange vectors of size of the ODE system regularly, specialized solvers can reduce communication costs by exchanging only the few vector elements that are actually required. In this work, parallel implementations of IRK methods for distributed address space are considered. At first general implementations for dense ODE systems are presented. Afterwards their communication will be optimized for sparse ODE systems and problems with limited access distance. The resulting implementations are examined in terms of runtime and scalability. Therefore measurements made on different computer systems using different sparse ODE systems will be evaluated. It is found that one implementation option is particularly well suited for sparse ODE systems as well for problems with limited access distance. Zusammenfassung Iterierte Runge-Kutta (IRK) Verfahren sind eine Klasse von Lösungsverfahren für Anfangswertprobleme gewöhnlicher Differentialgleichungssysteme (DGL), welche ein hohes Parallelisierungspotential besitzen. Während Implementierungen für dichtbesetzte DGL regelmäßig Vektoren der Größe der DGL austauschen müssen, können spezialisierte Löser Kommunikationskosten einsparen, indem nur die wenigen tatsächlich benötigten Vektorelemente ausgetauscht werden. In dieser Arbeit werden parallele Implementierungen von IRK-Verfahren für verteilten Adressraum betrachtet. Es werden zunächst allgemeine Implementierungen für dichtbesetzte DGL vorgestellt. Anschließend wird deren Kommunikation für dünnbesetzte DGL und Probleme mit beschränkter Zugriffsdistanz optimiert. Die entstandenen Implementierungen werden in Hinsicht auf Laufzeit und Skalierbarkeit untersucht. Dafür werden Messungen auf verschiedenen Rechnersystemen mit unterschiedlichen dünnbesetzten DGL ausgewertet. Dabei wird festgestellt, dass eine Implementierung der Kommunikation sowohl für dünnbesetzte DGL als auch für Probleme mit beschränkter Zugriffsdistanz besonders gut geeignet ist.
Media and Symbols: The Forms of Expression, Communication, and Education

Theories of Learning and Instruction Edited by Ernest R. Hilgard This yearbook evaluates the contribution of learning theory and experimentation on learning to the technology of education. It includes chapters dealing with educational technology, a discussion of a psychology of teaching, a review of the meaning-of readiness as it affects school practices, and other chapters that focus on concrete problems of educational reform and instruction. NSSE 63rd Yearbook, Part I 1974 xi, 430 pages Paper $4.50
Jacques Pitrat, l'Intelligence Artificielle et les Jeux

Cet article décrit les apports de Jacques Pitrat à la programmation d’intelligences artificielles pour les jeux ainsi que les travaux qui ont puisé leur inspiration dans ses recherches. L’article commence par évoquer le General Game Playing, puis viennent ensuite l’apprentissage par généralisation, l’amorçage, la dualité entre politique et évaluation et une évocation des thèses sur les jeux dirigées par Jacques Pitrat. Mots clés : Intelligence Artificielle, Jeux, General Game Playing, Apprentissage Automatique, Amorçage
Effects of Altitude and Leaf Age on Leaf Shape in an Alpine Shrub: The Relevance for the Leaf Area Estimation Model

ABSTRACT Nondestructive methods to estimate leaf area (LA) by leaf length (L) and/or width (W) are useful in plant physiology and ecology studies. However, both environmental and ontogenic factors may influence leaf size and/or shape, which may alter the coefficient of LA models. We carried an investigation along an altitudinal gradient in the Sergyemla Mountains, southeast Tibet. In August 2009, we selected nine sites at about every 50 m in altitude from 4,250 m to 4,640 m a.s. l. A total of 4,245 different leaf-aged Rhododendron aganniphum var. schizopeplum (a dominant overstory species) leaves were measured. Compared with the single dimensional models, the two-dimensional model encompassing both L and W (model 5) reflected higher R2 (0.98–0.99) and lower MSE (1.19–3.21) across different leaf age groups for each site, implying that such model could provide the best fit for LA estimation. Analysis of covariance further illustrated that two leaf dimensions model was irrespective of leaf age effects in eight out of the nine sites. Leaf shape (L:W ratio) varied between sites and tended to decrease at higher altitudes (4500–4640 m a.s.l.), leading to significant differences in coefficients of the two-dimension model between every two adjacent sites. For overstory species in alpine habitats, altitude rather than leaf age may affect leaf shape which alters the coefficients of LA estimation models. Since leaf shape of different species (overstory species versus understory ones) may show different responses to a certain environmental gradient, researchers must pay attention to the variation of leaf shape when estimating species-specific LA by measuring L and W, especially when leaves of the top overstory species were collected at different sites.
On complete orbit spaces of SL(2) actions, II

The aim of this paper is to extend the results of [BB-Ś2] concerning geometric quotients of actions of SL(2) to the case of good quotients. Thus the results of the present paper can be applied to any action of SL(2) on a complete smooth algebraic variety, while the theorems proved in [BB-Ś2] concerned only special situations. Like in [BB-Ś2], the source of our study lies in Mumford’s Geometric Invariant Theory [GIT]. His results concerning semi-stability lead to the Conjecture (see below). In order to state it we need the following definition:
Improved Determination of the CKM Angle $\alpha$ from $B \to \pi\pi$ decays

Motivated by a recent paper that compares the results of the analysis of the CKM angle alpha in the frequentist and in the Bayesian approaches, we have reconsidered the information on the hadronic amplitudes, which helps constraining the value of alpha in the Standard Model. We find that the Bayesian method gives consistent results irrespective of the parametrisation of the hadronic amplitudes and that the results of the frequentist and Bayesian approaches are equivalent when comparing meaningful probability ranges or confidence levels. We also find that from B to pi pi decays alone the 95% probability region for alpha is the interval [80^o,170^o], well consistent with recent analyses of the unitarity triangle where, by using all the available experimental and theoretical information, one gets alpha = (93 +- 4)^o. Last but not least, by using simple arguments on the hadronic matrix elements, we show that the unphysical region alpha ~ 0, present in several experimental analyses, can be eliminated.
The politics of co-implementation and their potential in shaping egalitarian cities

This paper contributes to the debate on co-production in planning theory and practice by examining the political agency of non-profit housing actors—here termed “alternative co-producers”—in building an egalitarian city. Drawing from theories of co-production, planning politics, democracy and governance, the paper introduces, theorizes and operationalizes the concept of “co-implementation” as the political moment in which egalitarian cities can be jointly shaped by public authorities, co-producers and the civil society. By egalitarian cities, we mean cities governed by a reinvented democratic arrangement that can better realize policy and planning goals in the direction of “housing for all. ” To test the political agency of alternative co-producers in their role as plan co-implementers, the paper relies on empirical evidence from HousingNOLA; a 10-year strategy and implementation plan in post-Katrina New Orleans. By critically examining the politics of HousingNOLA during the first 3 years of its implementation (2015–2018), the paper reveals the political conditions and practices that have favored or hampered co-implementation in New Orleans and that have determined progress in realizing an egalitarian city.
ONION: Joint Unsupervised Feature Selection and Robust Subspace Extraction for Graph-based Multi-View Clustering

Graph-based Multi-View Clustering (GMVC) has received extensive attention due to its ability to capture the neighborhood relationship among data points from diverse views. However, most existing approaches construct similarity graphs from the original multi-view data, the accuracy of which heavily and implicitly relies on the quality of the original multiple features. Moreover, previous methods either focus on mining the multi-view commonality or emphasize on exploring the multi-view individuality, making the rich information contained in multiple features cannot be effectively exploited. In this work, we design a novel GMVC framework via cOmmoNality and Individuality discOvering in lateNt subspace (ONION), seeking for a robust and discriminative subspace representation compatible across multiple features for GMVC. To be specific, our method simultaneously formulates the unsupervised sparse feature selection and the robust subspace extraction, as well as the target graph learning in a unified optimization model, which can help the learning of the discriminative subspace representation and the target graph in a mutual reinforcement manner. Meanwhile, we manipulate the target graph by an explicit structural penalty, rendering the connected components in the graph directly reveal clusters. Experimental results on seven benchmark datasets demonstrate the effectiveness of our proposed method.
Mutual Authentication of Devices under Multi-Cluster Environment in Industrial Internet of Things (IIoT) Networks

Wireless Sensor Nodes have become crucial part of Industrial Internet of Things (IIoT) applications currently being designed. Many innovative and practical solutions are given to the problems which belongs to heterogenous and integrated domains. With aggressive use of wireless sensors in IIoT environments, concern of security has increased. As communications between the devices in IIoT environment involve exchange of information that is crucial and sensitive which must be secure transactions. Many security protocols have been proposed using various cryptographic techniques, but there is extra constraint on computational resources of sensor devices which makes it difficult to apply standard security protocols. In this paper, we proposed a security protocol in which we use hierarchal architecture of wireless sensor networks. A group of devices in a region are considered as a cluster, each cluster contains a Cluster Head through which a sensor device requests a token from server for mutual authentication both in inter/intra cluster environment. This token contains combined computed hash of Identity of Device, Cluster Head and Server along with random number generated by device. This token-based authentication provides secure communication between devices because of added validity and timestamps in the event of any attack. This proposed protocol is verified as safe by BAN logic and AVISPA.
Minimizing Cold Start Times in Serverless Deployments

Serverless deployments of Cloud Applications involve containerizing an application that then remains dormant(cold) until a trigger event like a user visiting an endpoint occurs. The host machine then provisions this dormant container into a Virtual Machine that serves the request and then stays idle, waiting for subsequent requests to come in(warm). While the performance for requests made while a container is warm is indistinguishable from a fully managed server stack, requests when a container is cold can take several seconds because of the overheads involved in VM provisioning. The time at which a container goes from warm to cold is decided by the host VM depending on existing load and it’s configuration. This paper aims to come up with methods to reduce the frequency and duration of cold starts occurring across different workloads and cloud providers. By changing base images, lazy loading I/O and DB initializations and modifying CPU capacity the cold start times on GCP Cloud Run were reduced by upto 5% and 10.5% for simple and database dependent workloads respectively.
A Statistical Study of Massive Cluster-forming Clumps

We report results of the observations of 15 regions in several molecular lines for a statistical study of massive cluster-forming clumps. We identified 24 clumps based on the C18O (J = 1–0) data obtained by the NRO 45 m telescope, and found that 16 of them are associated with young clusters. The clumps associated with clusters have a typical mass, radius, and molecular density of ∼1 × 103 M☉, ∼0.5 pc, ∼1 × 105 cm−3, respectively. We categorized the clumps and clusters into four types according to the spatial coincidence of gas and star density, and discussed their evolutions: Clumps without clusters (Type 1), clumps showing good correlations with clusters (Type 2), clumps showing poor correlations with clusters (Type 3), and clusters with no associated clumps (Type 4). Analyses of the velocity structures and the chemical compositions imply that the clump + cluster systems should evolve from Type 1 to Type 4. We found that some of the Type 2 clumps are infalling on the clump-scale to form clusters at the clump center, which should commonly occur in the beginning of cluster formation. Interestingly, all of the identified Type 1 clumps are likely to be older than the Type 2 clumps in terms of chemical compositions, suggesting that they have been gravitationally stable for a long time, possibly being supported by the strong magnetic field of ≳1 mG. Type 1 clumps younger than the observed Type 2 clumps should be very rare to find because of their short lifetime.
Deep or statistical: an empirical study of traffic predictions on multiple time scales

Traffic prediction aims to forecast the future traffic level based on past observations. In this paper, we conduct an empirical study of traffic prediction for a campus trace on different time scales and get the following conclusions: 1) deep learning performs well on coarser time scales; 2) with a finer-granularity of time or insufficient data, statistical and regressive models outperform; 3) For a one-week trace, the granularity of 5 minutes has the strongest predictability.
Density based geospatial clustering: Methods, Applications and Future Directions

The emergence of the novel corona virus disease (COVID-19) since 2019 has been a cause of significant concern for people throughout the world. While tremendous effort has been put in to it by healthcare facilities, both public and private, it would not be a stretch to state that the resources allotted were not enough to handle the floods of covid and the non-covid patients at the same time. As the entire world was under lockdown, it was considerably tougher for people to move around. This meant getting check-ups for covid was fairly tough. Thus, building up many hospital camps around a city became important. In this article, the locations of different healthcare institutions and residential flats in and around the city of Bhubaneswar were analysed. Clusters were generated out of highly dense regions utilising a number of unsupervised learning density based clustering techniques and the best model was picked among them. Folium leaflet maps in Python were used to show the clusters created from the best performing clustering method. This would allow us to collect crucial information identifying areas in severe need of medical attention. Thus, resources can be divided evenly among the population with the information acquired.