Magnetic and structural transitions in layered FeAs systems: AFe2As2 versus RFeAsO compounds

Resistivity, specific heat and magnetic susceptibility measurements performed on SrFe2As2 samples evidence a behavior very similar to that observed in LaFeAsO and BaFe2As2 with the difference that the formation of the SDW and the lattice deformation occur in a pronounced first order transition at T_0=205K. Comparing further data evidences that the Fe-magnetism is stronger in SrFe2As2 and in EuFe2As2 than in the other layered FeAs systems investigated up to now. Full potential LDA band structure calculations confirm the large similarity between the compounds, especially for the relevant low energy Fe 3d states. The relation between structural details and magnetic order is analyzed.Comment: 4 pages, 3 figure

Strong coupling between magnetic and structural order parameters in SrFe2As2

X-ray and Neutron diffraction as well as muon spin relaxation and M\"ossbauer experiments performed on SrFe$_2$As$_2$ polycrystalls confirm a sharp first order transition at $T_0 = 205$,K corresponding to an orthorhombic phase distortion and to a columnar antiferromagnetic Fe ordering with a propagation vector (1,0,1), and a larger distortion and larger size of the ordered moment than reported for BaFe$_2$As$_2$. The structural and the magnetic order parameters present an remarkable similarity in their temperature dependence from $T_0$ down to low temperatures, showing that both phenomena are intimately connected. Accordingly, the size of the ordered Fe moments scale with the lattice distortion when going from SrFe$_2$As$_2$ to BaFe$_2$As$_2$. Full-potential band structure calculations confirm that the columnar magnetic order and the orthorhombic lattice distortion are intrinsically tied to each other.Comment: 10 pages, 4 figure

Electronic structure of SrPt_4Ge_{12}: a combined photoelectron spectroscopy and band structure study

We present a combined study of the electronic structure of the superconducting skutterudite derivative SrPt4Ge12 by means of X-ray photoelectron spectroscopy and full potential band structure calculations including an analysis of the chemical bonding. We establish that the states at the Fermi level originate predominantly from the Ge 4p electrons and that the Pt 5d shell is effectively full. We find excellent agreement between the measured and the calculated valence band spectra, thereby validating that band structure calculations in combination with photoelectron spectroscopy can provide a solid basis for the modeling of superconductivity in the compounds MPt4Ge12 (M = Sr, Ba, La, Pr) series

Simple Metals at High Pressure

In this lecture we review high-pressure phase transition sequences exhibited by simple elements, looking at the examples of the main group I, II, IV, V, and VI elements. General trends are established by analyzing the changes in coordination number on compression. Experimentally found phase transitions and crystal structures are discussed with a brief description of the present theoretical picture.Comment: 22 pages, 4 figures, lecture notes for the lecture given at the Erice course on High-Pressure Crystallography in June 2009, Sicily, Ital

AFe2As2 (A = Ca, Sr, Ba, Eu) and SrFe_(2-x)TM_(x)As2 (TM = Mn, Co, Ni): crystal structure, charge doping, magnetism and superconductivity

The electronic structure and physical properties of the pnictide compound families $RE$OFeAs ($RE$ = La, Ce, Pr, Nd, Sm), $A$Fe$_{2}$As$_{2}$ ($A$ = Ca, Sr, Ba, Eu), LiFeAs and FeSe are quite similar. Here, we focus on the members of the $A$Fe$_{2}$As$_{2}$ family whose sample composition, quality and single crystal growth are better controllable compared to the other systems. Using first principles band structure calculations we focus on understanding the relationship between the crystal structure, charge doping and magnetism in $A$Fe$_{2}$As$_{2}$ systems. We will elaborate on the tetragonal to orthorhombic structural distortion along with the associated magnetic order and anisotropy, influence of doping on the $A$ site as well as on the Fe site, and the changes in the electronic structure as a function of pressure. Experimentally, we investigate the substitution of Fe in SrFe$_{2-x}TM_{x}$As$_{2}$ by other 3$d$ transition metals, $TM$ = Mn, Co, Ni. In contrast to a partial substitution of Fe by Co or Ni (electron doping) a corresponding Mn partial substitution does not lead to the supression of the antiferromagnetic order or the appearance of superconductivity. Most calculated properties agree well with the measured properties, but several of them are sensitive to the As $z$ position. For a microscopic understanding of the electronic structure of this new family of superconductors this structural feature related to the Fe-As interplay is crucial, but its correct ab initio treatment still remains an open question.Comment: 27 pages, single colum

Analysis of parcel-based image classification methods for monitoring the activities of the Land Bank of Galicia (Spain)

[EN] The abandonment of agricultural plots entails a low economic productivity of the land and a higher vulnerability to wildfires and degradation of affected areas. In this sense, the local government of Galicia is promoting new methodologies based on high-resolution images in order to classify the territory in basic and generic land uses. This procedure will be used to control the sustainable management of plots belonging to the Land Bank. This paper presents an application study for maintaining and updating land use/land cover geospatial databases using parcel-oriented classification. The test is performed over two geographic areas of Galicia, in the northwest of Spain. In this region, forest and shrublands in mountain environments are very heterogeneous with many private unproductive plots, some of which are in a high state of abandonment. The dataset is made of high spatial resolution multispectral imagery, cadastral cartography employed to define the image objects (plots), and field samples used to define evaluation and training samples. A set of descriptive features is computed quantifying different properties of the objects, i.e. spectral, texture, structural, and geometrical. Additionally, the effect on the classification and updating processes of the historical land use as a descriptive feature is tested. Three different classification methodologies are analyzed: linear discriminant analysis, decision trees, and support vector machine. The overall accuracies of the classifications obtained are always above 90 % and support vector machine method is proved to provide the best performance. Forest and shrublands areas are especially undefined, so the discrimination between these two classes is low. The results enable to conclude that the use of automatic parcel-oriented classification techniques for updating tasks of land use/land cover geospatial databases, is effective in the areas tested, particularly when broad and well defined classes are required.The authors appreciate the collaboration and support provided by Xunta de Galicia, Sociedade para o Desenvolvemento Comarcal de Galícia, and Banco de Terras de Galicia. The financial support provided by the Spanish Ministerio de Ciencia e Innovación in the framework of the projects CGL2010-19591/BTE and CGL2009-14220 is also acknowledged.Hermosilla, T.; Díaz Manso, J.; Ruiz Fernández, LÁ.; Recio Recio, JA.; Fernández-Sarría, A.; Ferradáns Nogueira, P. (2012). Analysis of parcel-based image classification methods for monitoring the activities of the Land Bank of Galicia (Spain). Applied Geomatics. 4(4):245-255. https://doi.org/10.1007/s12518-012-0087-zS24525544Arikan M (2004) Parcel-based crop mapping through multi-temporal masking classification of landsat 7 images in Karacabey, Turkey. Int Arch Photogramm Remote Sens Spat Inf Sci 35:1085–1090Balaguer A, Ruiz LA, Hermosilla T, Recio JA (2010) Definition of a comprehensive set of texture semivariogram features and their evaluation for object-oriented image classification. Comput Geosci 36(2):231–240Balaguer-Besser A, Hermosilla T, Recio JA, Ruiz LA (2011) Semivariogram calculation optimization for object-oriented image classification. Model Sci Educ Learn 4(7):91–104Blaschke T (2010) Object based image analysis for remote sensing. ISPRS J Photogramm 65(1):2–16Cohen Y, Shoshany M (2000) Integration of remote sensing, GIS and expert knowledge in national knowledge-based crop recognition in Mediterranean environment. Int Arch Photogramm Remote Sens 33(Part B7):280–286Congalton R (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37(1):35–46Dadhwal VK, Singh RP, Dutta S, Parihar JS (2002) Remote sensing based crop inventory: a review of Indian experience. Trop Ecol 43(1):107–122De Wit AJW, Clevers JGPW (2004) Efficiency and accuracy of per-field classification for operational crop mapping. Int J Remote Sens 25:4091–4112Del Frate F, Pacifici F, Solimini D (2008) Monitoring urban land cover in Rome, Italy, and its changes by single-polarization multitemporal SAR images. IEEE J Sel Top Appl Earth Obs Remote Sens 1:87–97Díaz-Manso JM, Ferradáns-Nogueira P (2011) Modelo de uso actual da terra. In: Cobelle-Rico EJ, Diaz-Manso JM, Crecente-Maseda R, Martínez-Rivas EM (eds) Mercado e Mobilidade de Terras en Galícia, 1st edn. Servizo de Publicacións e Intercambio Científico, Santiago de Compostela, Spain, pp 31–44Dupas CA (2000) SAR and LANDSAT TM image fusion for land cover classification in the Brazilian Atlantic Forest Domain. Int Arch Photogramm Remote Sens XXXIII(Part B1):96–103El Kady M, Mack CB (1992) Remote sensing for crop inventory of Egypt’s old agricultural lands. Int Arch Photogramm Remote Sens 29:176–185Everitt BS, Dunn G (2001) Applied multivariate data analysis, 2nd edn. Edward Arnold, LondonHaralick RM, Shanmugam K, Dinstein I (1973) Texture features for image classification. IEEE Transact Syst Man Cybern 3(6):610–622Hermosilla T, Almonacid J, Fernández-Sarría A, Ruiz LA, Recio JA (2010) Combining features extracted from imagery and lidar data for object-oriented classification of forest areas. Int Arch Photogramm Remote Sens Spat Inf Sci 38(4/C7)Hernández Orallo J, Ramírez Quintana MJ, Ferri Ramírez C (2004) Introducción a la minería de datos. Pearson Educación S.A, MadridHomer C, Huang C, Yang L, Wylie B, Coan M (2004) Development of a 2001 National Land-Cover Database for the United States. Photogramm Eng Remote Sens 70:829–840Huberty CJ (1994) Applied discriminant analysis. Wiley, New YorkLaws KI (1985) Goal-directed texture image segmentation. Appl Artif Intel II, SPIE 548:19–26Ormeci C, Alganci U, Sertel E (2010) Identification of crop areas using SPOT-5 data, FIG Congress 2010 Facing the Challenges—building the capacity. Sydney, Australia, pp 11–16Peled A, Gilichinsky M (2004) GIS-driven analyses of remotely sensed data for quality assessment of existing land cover classification. Int Arch Photogramm Remote Sens Spat Inf Sci 35Peled A, Gilichinsky M (2010) Knowledge-based classification of land cover for the quality assessment of GIS database. Int Arch Photogramm Remote Sens Spat Inf Sci 38:217–222Perveen F, Nagasawa R, Ali S, Husnain (2008) Evaluation of ASTER spectral bands for agricultural land cover mapping using pixel-based and object-based classification approaches. Int Arch Photogramm Remote Sens Spat Inf Sci 37(4-C1)Petit CC, Lambin EF (2002) Impact of data integration technique on historical land-use/land-cover change: comparing historical maps with remote sensing data in the Belgian Ardennes. Landsc Ecol 17:117–132Quinlan JR (1993) C4.5: Programs for machine learning. Kaufmann, San FranciscoRabe A, van der Linden S, Hostert P (2010) imageSVM, Version 2.1. www.hu-geomatics.deRecio JA, Hermosilla T, Ruiz LA, Fernández-Sarría A (2011) Historical land use as a feature for image classification. Photogramm Eng Remote Sens 77(4):377–387Ruiz LA, Fernández-Sarría A, Recio JA (2004) Texture feature extraction for classification of remote sensing data using wavelet decomposition: a comparative study. Int Arch Photogramm Remote Sens Spat Inf Sci 35(B4):1109–1115Ruiz LA, Recio JA, Hermosilla T, Fdez. Sarriá A (2009) Identification of agricultural and land cover database changes using object-oriented classification techniques. 33rd International Symposium on Remote Sensing of Environment, May 4–8, Stresa (Italy)Ruiz LA, Recio JA, Fernández-Sarría A, Hermosilla T (2011) A feature extraction software tool for agricultural object-based image analysis. Comput Electron Agric 76(4):284–296Tansey K, Chambers I, Anstee A, Denniss A, Lamb A (2009) Object-oriented classification of very high resolution airborne imagery for the extraction of hedgerows and field margin cover in agricultural areas. Appl Geogr 29(2):145–157van der Linden S, Rabe A, Wirth F, Suess S, Okujeni A, Hostert P (2010) imageSVM regression, application manual: imageSVM version 2.1. Humboldt-Universität zu Berlin, GermanyVapnik VN (1998) Statistical learning theory. Wiley, New YorkWalsh SJ, McCleary AL, Mena CF, Shao Y, Tuttle JP, Gonzalez A, Atkinson R (2008) QuickBird and Hyperion data analysis of an invasive plant species in the Galapagos Islands of Ecuador: implications for control and land use management. Remote Sens Environ 112(5):1927–1941Walter V (2004) Object-based classification of remote sensing data for change detection. ISPRS J Photogramm Remote Sens 58:225–238Walter V (2005) Object-based evaluation of lidar and multiespectral data for automatic change detection in GIS databases. Geo-Inf Syst 18:10–15Zaragozí, B, Rabasa, A, Rodríguez-Sala, JJ, Navarro, JT, Belda, A, Ramón, A (2012) Modelling farmland abandonment: A study combining GIS and data mining techniques. Agric Ecosys Environ 155:124–132Zhang S, Liu X (2005) Realization of data mining model for expert classification using multi-scale spatial data. Int Arch Photogramm Remote Sens Spat Inf Sci 26(4/W6):107–11

Quantifying unpredictability: A multiple-model approach based on satellite imagery data from Mediterranean ponds.

Fluctuations in environmental parameters are increasingly being recognized as essential features of any habitat. The quantification of whether environmental fluctuations are prevalently predictable or unpredictable is remarkably relevant to understanding the evolutionary responses of organisms. However, when characterizing the relevant features of natural habitats, ecologists typically face two problems: (1) gathering long-term data and (2) handling the hard-won data. This paper takes advantage of the free access to long-term recordings of remote sensing data (27 years, Landsat TM/ETM+) to assess a set of environmental models for estimating environmental predictability. The case study included 20 Mediterranean saline ponds and lakes, and the focal variable was the water-surface area. This study first aimed to produce a method for accurately estimating the water-surface area from satellite images. Saline ponds can develop salt-crusted areas that make it difficult to distinguish between soil and water. This challenge was addressed using a novel pipeline that combines band ratio water indices and the short near-infrared band as a salt filter. The study then extracted the predictable and unpredictable components of variation in the water-surface area. Two different approaches, each showing variations in the parameters, were used to obtain the stochastic variation around a regular pattern with the objective of dissecting the effect of assumptions on predictability estimations. The first approach, which is based on Colwell's predictability metrics, transforms the focal variable into a nominal one. The resulting discrete categories define the relevant variations in the water-surface area. In the second approach, we introduced General Additive Model (GAM) fitting as a new metric for quantifying predictability. Both approaches produced a wide range of predictability for the studied ponds. Some model assumptions-which are considered very different a priori-had minor effects, whereas others produced predictability estimations that showed some degree of divergence. We hypothesize that these diverging estimations of predictability reflect the effect of fluctuations on different types of organisms. The fluctuation analysis described in this manuscript is applicable to a wide variety of systems, including both aquatic and nonaquatic systems, and will be valuable for quantifying and characterizing predictability, which is essential within the expected global increase in the unpredictability of environmental fluctuations. We advocate that a priori information for organisms of interest should be used to select the most suitable metrics estimating predictability, and we provide some guidelines for this approach

Feshbach resonances and mesoscopic phase separation near a quantum critical point in multiband FeAs-based superconductors

High Tc superconductivity in FeAs-based multilayers (pnictides), evading temperature decoherence effects in a quantum condensate, is assigned to a Feshbach resonance (called also shape resonance) in the exchange-like interband pairing. The resonance is switched on by tuning the chemical potential at an electronic topological transition (ETT) near a band edge, where the Fermi surface topology of one of the subbands changes from 1D to 2D topology. We show that the tuning is realized by changing i) the misfit strain between the superconducting planes and the spacers ii) the charge density and iii) the disorder. The system is at the verge of a catastrophe i.e. near a structural and magnetic phase transition associated with the stripes (analogous to the 1/8 stripe phase in cuprates) order to disorder phase transition. Fine tuning of both the chemical potential and the disorder pushes the critical temperature Ts of this phase transition to zero giving a quantum critical point. Here the quantum lattice and magnetic fluctuations promote the Feshbach resonance of the exchange-like anisotropic pairing. This superconducting phase that resists to the attacks of temperature is shown to be controlled by the interplay of the hopping energy between stripes and the quantum fluctuations. The superconducting gaps in the multiple Fermi surface spots reported by the recent ARPES experiment of D. V. Evtushinsky et al. arXiv:0809.4455 are shown to support the Feshbach scenario.Comment: 31 pages, 7 figure

Community Surveillance of Omicron in Ontario: Wastewater-based Epidemiology Comes of Age

Wastewater-based surveillance of SARS-CoV-2 RNA has been implemented at building, neighbourhood, and city levels throughout the world. Implementation strategies and analysis methods differ, but they all aim to provide rapid and reliable information about community COVID-19 health states. A viable and sustainable SARS-CoV-2 surveillance network must not only provide reliable and timely information about COVID-19 trends, but also provide for scalability as well as accurate detection of known or unknown emerging variants. Emergence of the SARS-CoV-2 variant of concern Omicron in late Fall 2021 presented an excellent opportunity to benchmark individual and aggregated data outputs of the Ontario Wastewater Surveillance Initiative in Canada; this public health-integrated surveillance network monitors wastewaters from over 10 million people across major population centres of the province. We demonstrate that this coordinated approach provides excellent situational awareness, comparing favourably with traditional clinical surveillance measures. Thus, aggregated datasets compiled from multiple wastewater-based surveillance nodes can provide sufficient sensitivity (i.e., early indication of increasing and decreasing incidence of SARS-CoV-2) and specificity (i.e., allele frequency estimation of emerging variants) with which to make informed public health decisions at regional- and state-levels.Ontario Ministry of the Environment, Conservation and Parks|| Genome Canada and Ontario Genomics (OGI-209)||NSERC (ALLRP 555041-20 to C.O.)||Ontario Clean Water Agenc