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Despite modern preparation techniques offer the opportunity to tailor the composition, size, and shape of magnetic nanoparticles, understanding and hence controlling the magnetic properties of such entities remains a challenging task, due to the complex interplay between the volume-related properties and the phenomena occurring at the particle’s surface. The present work investigates spinel iron oxide nanoparticles as a model system to quantitatively analyze the crossover between the bulk and the surface-dominated magnetic regimes. The magnetic properties of ensembles of nanoparticles with an average size in the range of 5–13 nm are compared. The role of surface anisotropy and the effect of oleic acid, one of the most common and versatile organic coatings, are discussed. The structural and morphological properties are investigated by X-ray diffraction and transmission electron microscopy. The size dependence of the surface contribution to the effective particle anisotropy and the magnetic structure are analyzed by magnetization measurements and in-field Mössbauer spectrometry. The structural data combined with magnetometry and Mössbauer spectrometry analysis are used to shed light on this complex scenario revealing a crossover between volume and surface-driven properties in the range of 5–7 nm. Graphical Abstract

Arsenate, As(V), and arsenite, As(III), are the most available arsenicals present in the soil solutions, in particular in mine polluted substrates, and cause several symptoms of toxicity in plants (like inhibition of seed germination and reduction of seedling development). For these reasons, seeds germination studies are essential for the design of phytoremediation activities of mine sites. Seed germination and seedling development of

In this work, industrial waste hexafluorosilicic acid (H2SiF6 or FSA) has been proven to be a low-cost alternative to silicate esters for the synthesis of high-quality MCM-41 (high surface area, hi...

Iron oxides/oxyhydroxides, namely maghemite, iron oxide-silica composite, akaganeite, and ferrihydrite, are studied for As

Arsenic pollution in ground and drinking water is a major problem worldwide due to the natural abundance of arsenic by dissolution from ground sediment or mining activities from anthropogenic activities. To overcome this issue, iron oxides as low-cost and non-toxic materials, have been widely studied as efficient adsorbents for arsenic removal, including when dispersed within porous silica supports. In this study, two head-to-head comparisons were developed to highlight the As(V)-adsorptive ability of meso- and macrostructured silica-based adsorbents. First, the role of the textural properties of a meso-(SBA15) and macrostructured (MOSF) silica support in affecting the structural-morphological features and the adsorption capacity of the active phase (Fe2O3) have been studied. Secondly, a comparison of the arsenic removal ability of inorganic (Fe2O3) and organic (amino groups) active phases was carried out on SBA15. Finally, since silica supports are commonly proposed for both environmental and biomedical applications as active phase carriers, we have investigated secondary silicon and iron pollution. The batch tests at different pH revealed better performance from both Fe2O3-composites at pH 3. The values of qm of 7.9 mg g−1 (53 mg gact−1) and 5.5 mg g−1 (37 mg gact−1) were obtained for SBA15 and MOSF, respectively (gact stands for mass of the active phase). The results suggest that mesostructured materials are more suitable for dispersing active phases as adsorbents for water treatment, due to the obtainment of very small Fe2O3 NPs (about 5 nm). Besides studying the influence of the pore size of SBA15 and MOSF on the adsorption process, the impact of the functionalization was analyzed on SBA15 as the most promising sample for As(V)-removal. The amino-functionalized SBA15 adsorbent (3-aminopropyltriethoxysilane, APTES) exhibited a qm of 12.4 mg g−1 and faster kinetics. Furthermore, issues associated with the release of iron and silicon during the sorption process, causing secondary pollution, were evaluated and critically discussed.

Nanostructured spinel ferrites with formula M2+Fe2O4 (where M2+ ═ Fe, Co, Mn, Ni, Zn, etc.) represent a class of magnetic materials widely employed in different research fields, due to the possibility of finely tuning the magnetic properties by changing the chemical composition, size, shape, capping agents, etc. Herein, a review of the most common chemical synthesis in liquid media, distinguishing among aqueous and non-aqueous routes, is presented. The experimental procedure together with the mechanism, advantages and disadvantages, and examples are illustrated for each synthetic strategy.

An oleate-based solvothermal approach has been employed to produce pure CoFe₂O₄ and MnFe₂O₄ and mixed Co-Mn ferrites having manganese content in the range 0.13-0.65 and crystallite size of about 8-9 nm. The structural and magnetic properties, studied by powder X-ray diffraction and room temperature 57Fe Mossbauer spectroscopy, have allowed to ascertain the formation of a unique spinel phase in which cobalt and manganese are present and to get insights on the cation distribution and the correlated magnetic properties.

A Raman study on Zinc-substituted Cobalt ferrites, with different Zn(II) contents in each sample of formula: CoFe₂O₄ (1), Zn0.30Co0.70Fe2.00O₄ (2), Zn0.46Co0.54Fe2.02O₄ (3), Zn0.53Co0.47Fe2.02O₄ (4) is reported. These samples show the same crystallite size (∼6 nm), particle size (∼7 nm) and particle size distribution (∼20%) and they have been synthesized through heating up surfactant-assisted thermal decomposition of metalorganic precursors. The effect of Zn(II) substitution in the cationic distribution is investigated by using the known metal-oxygen vibrational modes in tetrahedral and octahedral sites. The presence of Zn(II) metal ion is determined through the band at 150 cm-1 (T2g(1) phonon mode), which is not present in the pure Co-ferrite, a blue-shift of the Eg vibrational mode depending on Zn(II)/Co(II) cationic distribution and a shoulder at ∼250 cm-1, which appears when zinc enters in the structure, and a broadening and a red-shift in the A1g phonon mode is observed in Raman spectra of 2-4 samples. Interestingly the latter represents a potential key probe to monitor the Zn(II) presence in Zn-substituted Co-ferrites.

Achieving metal-organic frameworks (MOFs) in the form of nanoparticles (NanoMOFs) represents a recent challenge due to the possibility to combine the intrinsic porosity of these materials with the nanometric dimension, a fundamental requirement for strategic biomedical applications. In this outlook we envision the current/future opportunities of the NanoMOFs in the field of biomedicine, with particular emphasis on (i) biocompatible MOFs composition; (ii) MOFs miniaturization and (iii) nanoMOFs applications.

In this work we study stationary linear time-series models, and construct and analyse “score-matching” estimators based on the Hyvarinen scoring rule. We consider two scenarios: a single series of increasing length, and an increasing number of independent series of fixed length. In the latter case there are two variants, one based on the full data, and another based on a sufficient statistic. We study the empirical performance of these estimators in three special cases, autoregressive (AR), moving average (MA) and fractionally differenced white noise (ARFIMA) models, and make comparisons with full and pairwise likelihood estimators. The results are somewhat model-dependent, with the new estimators doing well for MA and ARFIMA models, but less so for AR models.

Bayesian regression models have been widely studied and adopted in the statistical literature. Many studies consider the development of reliable priors to select the relevant variables and derive accurate posterior predictive distributions. Moreover in the context of small high-dimensional data, where the number of observations is very small with respect to the number of predictors, sparsity is assumed and many parameters can be set to values close to zero without affecting the fit of the model. Aim of this work is to develop a comparative analysis to empirically evaluate the performances of several Bayesian regression approaches in these contexts. In this study we assume that the predictors can be expressed only as binary variables coding the presence or the absence of a particular characteristic of the system. This binary structure is often present in many real studies, in particular in laboratory experimentation and in very high-dimension genome wide association studies.

An important Bronze Age settlement was discovered during an archaeological excavation in the Monte Meana karst cave in south-western Sardinia (Italy) between 2007 and 2012. In this region, the caves were used since the Neolithic for different purposes, such as burials or other rituals. The dig highlighted a rare example of domestic use of a cave and showed a case study of household space of the Early -Middle Bronze Age, at the beginning of the Nuragic civilization. This provided the opportunity to investigate through a multidisciplinary approach, the empirical knowledge of ancient potters and technological characters of local pottery production especially in relation to domestic use, in a context at that time devoid of external cultural interferences. For this purpose, a selection of 24 pottery sherds related to vessel forms for cooking, storage, and eating were studied through macroscopic surveys and archaeometric analysis by petrography, scanning electron microscopy, X-ray powder diffraction, and Fourier transform infrared spectroscopy. The results revealed some discriminant variables (shape, wall thickness, features of the paste, surface smoothing, presence of diagnostic mineralogical phases, and tempers), within the ceramic products of this Sardinian Bronze Age site, showing skillful management of firing temperatures.

Artificial nano-heterostructures (NHs) with controlled morphology, obtained by combining two or more components in several possible architectures, make them suitable for a wide range of applications. Here, we propose an oleate-based solvothermal approach to design silver-spinel ferrite flower-like NHs. Small oleate-coated silver nanoparticles were used as seeds for the growth of magnetic spinel ferrite (cobalt ferrite and spinel iron oxide) nanodomains on their surface. With the aim of producing homogeneous flower-like heterostructures, a careful study of the effect of the concentration of precursors, the reaction temperature, the presence of water, and the chemical nature of the spinel ferrite was carried out. The magnetic and optical properties of the NHs were also investigated. A heterogeneous growth of the spinel ferrite phase on the silver nanoparticles, through a possible defect-assisted mechanism, was suggested in the light of the high concentration of stacking faults (intrinsic and twins) in the silver seeds, revealed by Rietveld refinement of powder X-ray diffraction patterns and High-Resolution electron microscopy.

Building clusters for pattern recognition and analysis of geographical areas can be a useful way to provide relevant information for economic and social decisions. In this paper, we introduce a novel spatial clustering technique, called Bootstrap ClustGeo (BCG), which is a hierarchical approach, based on bootstrap techniques with spatial constraints. We evaluate the performance of the proposed approach BCG through some real case studies and simulations studies with different complexity, by computing Clustering Validation Measures (CVM) and then we compare the approach with the recently proposed ClustGeo (CG). These analyses exhibit the accuracy of BCG, also with respect to CG, in the presented applications, and highlight the great potentiality of this new clustering technique to provide meaningful information for spatial analysis.

In this work, the effect of the M41S support pore structure (hexagonal or cubic) and of the wall thickness of the silica mesochannels has been evaluated aimed at achieving more and more efficient and regenerable iron oxide-based sorbents for H2S removal at midtemperature. With this purpose, we set up a simple Pluronic-free synthetic strategy capable of producing silica supports with hexagonal (MCM-41) or cubic (MCM-48) pore structure with different wall thicknesses that have been used to fabricate the corresponding sorbents made up of iron oxide nanoparticles homogeneously dispersed into the mesochannels. The combined use of 57Fe-Mossbauer Spectroscopy and DC magnetometry has allowed for ascertaining the presence of maghemite in the form of ultrasmall nanoparticles in both composites and gives new insights on the influence of the different silica matrices on the active phase features. The performances of the sorbents have been evaluated at midtemperature (300 °C) through three repeated sulfidation and reg...

During the 2nd millennium BC anthropomorphic menhirs belonging to a 3rd millennium BC sanctuary were reused as building material in the Arasseda Nuraghe (Sardinia, Italy). To analyse the Arasseda menhirs and the local Monte Ironi geological samples (presenting similar visual features), chemical (pXRF, ICP-OES, ICP-MS), mineralogical-chemical (PXRD) and physical (Mohs hardness) measurements were performed. Through the experimental data, the menhirs source provenance and the technological properties (workability, durability) of the raw material chosen for sculptural purposes during Copper Age were investigated. To the authors’ knowledge this is the first archaeometric study on the Arasseda menhirs (the third on Sardinian menhirs) and one between the few recently developed on European megaliths.

An easy, low-cost, repeatable seed-mediated growth approach in solvothermal condition has been proposed to synthesize bimagnetic spinel ferrite core-shell heterostructures in the 10-20 nm particle size range. Cobalt ferrite and manganese ferrite nanoparticles (CoFe2O4 and MnFe2O4) have been coated with isostructural spinel ferrites like maghemite/magnetite, MnFe2O4, and CoFe2O4 with similar cell parameters to create different heterostructures. The conventional study of the structure, morphology, and composition has been combined with advanced techniques in order to achieve details on the interface at the nanoscale level. Clear evidence of the heterostructure formation have been obtained (i) indirectly by comparing the 57Fe Mossbauer spectra of the core-shell samples and an ad hoc mechanical mixture and (ii) directly by mapping the nanoparticles' chemical composition by electron energy loss spectroscopy (EELS) and energy-dispersive X-ray spectroscopy (EDX) in the scanning transmission electron microscopy mode (STEM). In addition, chemical-sensitive electron tomography in STEM-EDX mode has been applied in order to obtain detailed 3D images with a sub-nanometer spatial resolution.

MCM-41 is proposed to build mesostructured Fe2O3-based sorbents as an alternative to other silica or alumina supports for mid-temperature H2S removal. MCM-41 was synthesized as micrometric (MCM41_M) and nanometric (MCM41_N) particles and impregnated through an efficient two-solvent (hexane–water) procedure to obtain the corresponding γ-Fe2O3@MCM-41 composites. The active phase is homogeneously dispersed within the 2 nm channels in the form of ultrasmall maghemite nanoparticles assuring a high active phase reactivity. The final micrometric (Fe_MCM41_M) and nanometric (Fe_MCM41_N) composites were tested as sorbents for hydrogen sulphide removal at 300 °C and the results were compared with a reference sorbent (commercial unsupported ZnO) and an analogous silica-based sorbent (Fe_SBA15). MCM-41 based sorbents, having the highest surface areas, showed superior performances that were retained after the first sulphidation cycle. Specifically, the micrometric sorbent (Fe_MCM41_M) showed a higher SRC value than the nanometric one (Fe_MCM41_N), due to the low stability of the nanosized particles over time caused by their high reactivity. Furthermore, the low regeneration temperature (300–350 °C), besides the high removal capacity, renders MCM41-based systems an alternative class of regenerable sorbents for thermally efficient cleaning up processes in Integrated Gasification Combined Cycles (IGCC) systems.

Obsidian is a natural volcanic glass in which nanocrystalline and microcrystalline phases can coexist with the glassy one. In this paper, magnetic properties of Monte Arci obsidians are investigated by an experimental approach commonly applied to synthetic nanostructured materials and rarely to natural ones, and correlated with the mineralogical composition and microstructure. Among the different crystalline phases, the iron-containing components are found to be responsible for a great variety of magnetic behaviors, including paramagnetism, antiferromagnetism, ferromagnetism, and superparamagnetism. The combined use of powder X-ray diffraction (PXRD), Fe-57 Mossbauer spectroscopy, DC magnetometry, and transmission electron microscopy (TEM/HRTEM) provides new insights in the Monte Arci obsidian: (i) the presence of magnetite nanoparticles spread into the glassy matrix; (ii) the presence of an antiferromagnetic phase responsible for a discontinuity at about 45 K; (iii) exchange bias phenomena, for the first time revealed in obsidians, due to the coupling between the nanostructured ferrimagnetic phase and the antiferromagnetic one; (iv) Goldanskii-Karyagin effect (GKE) associated with biotite.

The versatility of a promising and repeatable oleate-based solvothermal approach has been explored through the synthesis of MnFe₂O₄ and

Currently many research problems are addressed by analysing datasets characterized by a huge number of variables, with a relatively limited number of observations, especially when data are generated by experimentation. Most of the classical statistical procedures for regression analysis are often inadequate to deal with such datasets as they have been developed assuming that the number of observations is larger than the number of the variables. In this work, we propose a new penalization procedure for variable selection in regression models based on Bootstrap group Penalties (BgP). This new family of penalization methods extends the bootstrap version of the LASSO approach by taking into account the grouping structure that may be present or introduced in the model. We develop a simulation study to compare the performance of this new approach with respect several existing group penalization methods in terms of both prediction accuracy and variable selection quality. The results achieved in this study show that the new procedure outperforms the other penalties procedures considered.

A multi-technique approach was carried out to investigate the provenance and technological properties of 34 anthropomorphic menhirs from Laconi (central Sardinia, Italy) dated back to the local Copper Age (III millennium BC). After visual inspection, geological materials coming from local ignimbritic outcrops were characterized by elemental and mineralogical analyses: portable X-Ray Fluorescence (pXRF), Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES), Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) and Powder X-Ray Diffraction (PXRD). Conversely, the menhirs were studied only by visual examination and non-destructive pXRF measurements. By means of a comparison between pXRF data collected on the menhirs and the lithological outcrops, it was possible to assign the prehistoric artifacts of Laconi to local geo-sources, taking into account geochemical intra- and inter-source variabilities. pXRF was demonstrated to be a useful tool for provenance studies while mineralogical analysis resulted fundamental for the technological aspects. The latter technique pointed out the main use of hardstones, probably owing to the durability they give to the menhirs. To the best of our knowledge, this is the first archaeometric study on Sardinian menhirs and one of the few recently developed on European megaliths.

Metal–organic frameworks (MOFs) have shown a great potential in biomedicine due to their promising applications in different fields, including drug delivery, thermometry, theranostics etc. In this context, the development of magnetic sub-micrometric or nanometric MOFs through miniaturization approaches of magnetic MOFs up to the nanoscale still represents a crucial step to fabricate biomedical probes, especially in the field of theranostic nanomedicine. Miniaturization processes have to be properly designed to tailor the size and shape of particles and to retain magnetic properties and high porosity in the same material, fundamental prerequisites to develop smart nanocarriers integrating simultaneously therapeutic and contrast agents for targeted chemotherapy or other specific clinical use. An overview of current trends on the design of magnetic nanoMOFs in the field of biomedicine, with particular emphasis on theranostics and bioimaging, is herein envisioned.

Mesostructured titania (Anatase) and silica (MCM-41) were proposed as supports to design highly active, selective, and regenerable FeIII-based nanostructured sorbents for mid-temperature H2S removal in a model sour syngas. The resulting sorbents (Fe–SiO2 and Fe–TiO2) were tested as H2S removers at 300 °C and exhibited high reactivity and regenerability over repeated sulfidation cycles, with the best sorption performances achieved by the silica-based sorbent. Specifically, Fe–SiO2 showed a constant sorption capacity of 19 ± 1 mgS gsorbent−1 after the first sulfidation cycle. Meanwhile, a lower sorption capacity of 10 ± 1 mgS gsorbent−1 was found for the Fe–TiO2 composite. As evidenced by combining 57Fe Mossbauer spectroscopy with DC magnetometry, the nature (amorphous or crystalline) and composition (SiO2 or TiO2) of the inorganic mesostructures played a crucial role in the formation of the ultrasmall FeIII-active phase: maghemite (γ-Fe2O3) and pseudobrookite (Fe2TiO5) in the case of silica and titania, respectively. Therefore, the performance can be mainly justified in the light of the different reactivity of the active phases (Fe2O3 vs Fe2TiO5). FeIII-active phase in the form of ultrasmall Fe2O3 nanoparticles (about 2 nm) homogeneously dispersed in a highly stable mesostructured silica support assured high reactivity (85%–100% of the active phase involvement) and regenerability in the mid-temperature range as for the sulfidation run during the repeated sulfidation cycles (steady performances) avoiding the common drawbacks of unsupported sorbents (unreacted core and sintering phenomena causing loss of activity).

We review the most common situations where one or some of the regularity conditions which underlie likelihood based parametric inference fail. Three main classes of problems will be treated: boundary problems, indeterminate parameters and singular information matrices, and change-point problems. The review is focused on the large- and small-sample properties of the likelihood ratio, though other approaches to hypothesis testing and connections to estimation will be mentioned in passing.

The paper proposes a methodological approach to design complex experiments for multi-objective optimization. The strategy is based on evolutionary statistical inference to search for the optimal values in high-dimensional experimental spaces. We developed this approach to study a particular molecular system and discover the best molecules to be proposed as candidate drugs.

Scoring rules give rise to methods for statistical inference and are useful tools to achieve robustness or reduce computations. Scoring rule inference is generally performed through first-order approximations to the distribution of the scoring rule estimator or of the ratio-type statistic. In order to improve the accuracy of first-order methods even in simple models, we propose bootstrap adjustments of signed scoring rule root statistics for a scalar parameter of interest in presence of nuisance parameters. The method relies on the parametric bootstrap approach that avoids onerous calculations specific of analytical adjustments. Numerical examples illustrate the accuracy of the proposed method.

In this paper we discuss higher-order asymptotic expansions for proper scoring rules generalizing results for likelihood quantities, but meanwhile bring in the difficulty caused by the failure of the information identity. In particular, we derive higher-order approximations to the distribution of the scoring rule estimator, of the scoring rule ratio test statistic and, for a scalar parameter of interest, of the signed scoring rule root statistic. From these expansions, a modified signed scoring rule root statistic is proposed. Examples are given illustrating the accuracy of the modified signed scoring rule root statistic with respect to first-order methods.

We propose a new generalization of the skew-normal distribution (Azzalini, 1985) referred to as the Kumaraswamy skew-normal. The new distribution is computationally more tractable than the Beta skew-normal distribution (Mameli and Musio, 2013) with which it shares some properties.

An ecofriendly, low-cost, one-pot solvothermal approach has been developed to prepare spherical magnetite nanoparticles with sizes in the 7–12 nm range capped with a dialkylamine. Iron isopropoxide, water vapor, absolute ethanol, oleic acid, and oleylamine were used as iron oxide precursor, hydrolysis agent, solvent and surfactants, respectively. The surfactants’ role was investigated and an accurate correlation among the synthetic parameters, the crystallographic phases, and both crystallite and particle size was found. The amounts of oleylamine and oleic acid and the temperature have been revealed to be the key parameters in order to tune particle size and their polydispersity. An in-depth study on the role of each surfactant has pointed out the fundamental role of the amine as a reduction promoter as demonstrated by using different amines and confirmed by Mossbauer measurements. A dual 1H NMR-Fourier transform infrared spectroscopy approach on selected experiments for the investigation of the capping a...

In this paper we present a methodology that can be used to design experiments of complex systems characterized by a huge number of variables. The strategy combines the evolutionary principles with the information provided by statistical models tailored to the problem under consideration. Here, we are concerned with the process of design molecules, which is a quite challenging problem due to the presence of a high number of variables with a binary structure. Recent works on clustering of binary data and variable selection in the high-dimensional setting allow to develop an approach capable of recovering useful information derived from the incorporation of a grouping structure into the model.

The possibility to finely control nanostructured cubic ferrites ((MFe2O4)-Fe-II) paves the way to design materials with the desired magnetic properties for specific applications. However, the strict and complex interrelation among the chemical composition, size, polydispersity, shape and surface coating renders their correlation with the magnetic properties not trivial to predict. In this context, this work aims to discuss the magnetic properties and the heating abilities of Zn-substituted cobalt ferrite nanoparticles with different zinc contents (ZnxCo1-xFe2O4 with 0 < x < 0.6), specifically prepared with similar particle sizes (similar to 7 nm) and size distributions having the crystallite size (similar to 6 nm) and capping agent amount of 15%. All samples have high saturation magnetisation (M-s) values at 5 K (>100 emu g(-1)). The increase in the zinc content up to x = 0.46 in the structure has resulted in an increase of the saturation magnetisation (Ms) at 5 K. High Ms values have also been revealed at room temperature (similar to 90 emu g(-1)) for both CoFe2O4 and Zn0.30Co0.70Fe2O4 samples and their heating ability has been tested. Despite a similar saturation magnetisation, the specific absorption rate value for the cobalt ferrite is three times higher than the Zn-substituted one. DC magnetometry results were not sufficient to justify these data, the experimental conditions of SAR and static measurements being quite different. The synergic combination of DC with AC magnetometry and Fe-57 Mossbauer spectroscopy represents a powerful tool to get new insights into the design of suitable heat mediators for magnetic fluid hyperthermia.

In this paper we study the Beta skew-normal distribution introduced by Mameli and Musio (2013). Some new properties of this distribution are derived including formulae for moments in particular cases and bi-modality properties. Furthermore, we provide expansions for its distribution and density functions. Bounds for the moments and the variance of the Beta skew-normal are derived. Some of the results presented in this work can be extended to the entire family of the Beta-generated distribution introduced by Jones (Test 13(1):1–43, 2004).

We consider the use of modern likelihood asymptotics in the construction of confidence intervals for the parameter which determines the skewness of the distribution of the maximum/minimum of an exchangeable bivariate normal random vector. Simulation studies were conducted to investigate the accuracy of the proposed methods and to compare them to available alternatives. Accuracy is evaluated in terms of both coverage probability and expected length of the interval. We furthermore illustrate the suitability of our proposals by means of two data sets, consisting of, respectively, measurements taken on the brains of 10 mono-zygotic twins and measurements of mineral content of bones in the dominant and non-dominant arms for 25 elderly women.

The aim of this article is to introduce a new family of distributions, which generalizes the skew normal distribution (SN). This new family, called Beta skew-normal (BSN), arises naturally when we consider the distributions of order statistics of the SN. The BSN can also be obtained as a special case of the Beta generated distribution (Jones (2004)). In this work we pay attention to three other generalizations of the SN distribution: the Balakrishnan skew-normal (SNB) (Balakrishnan (2002), as a discussant of Arnold and Beaver (2002), Gupta and Gupta (2004), Sharafi and Behboodian (2008)), the generalized Balakrishnan skew-normal (GBSN) (Yadegari et al. (2008)) and a two parameter generalization of the Balakrishnan skew-normal (TBSN) (Bahrami et al. (2009)). The above three extensions are related to the Beta skew-normal distribution for particular values of the parameters. The paper is organized as follows: after describing briefly, in Section 2, the skew-normal distribution, its generalizations and listing their most important properties, in Section 3 we present some generalizations of the Beta distribution. In the last Section we define the Beta skew-normal distribution, we present its properties and some special cases. In particular the BSN contains the Beta half-normal distribution (Pescim et al. (2010)) as limiting case. Besides, we investigate its shape properties. We derive its moment generating function and we also compute numerically the first moment, the variance, the skewness and the kurtosis. We present two different methods which allow to simulate a BSN distribution. We explore its relationships with the other generalizations of the SN. We give some results concerning the SNB distribution. In particular we derive the exact distributions of the largest order statistic from SNB with parameters m and 1 and the shortest order statistic SNB with parameters m and -1., 21 pages, 3 figures and 1 table

In this work we propose different spatial models to study hospital recruitment, including some potentially explanatory variables, using data from the hospital of Mulhouse a town located in the north-east of France. Interest is on the distribution over geographical units of the number of patients living in this geographical unit. Models considered are within the framework of Bayesian latent Gaussian models. Our response variable is assumed to follow a binomial distribution, with logit link, whose parameters are the population in the geographical unit and the corresponding risk. The structured additive predictor accounts for effects of various covariates in an additive way, including smoothing functions of the covariates (for example a spatial effect). To approximate posterior marginals, which are not available in closed form, we use integrated nested Laplace approximations (INLA), recently proposed for approximate Bayesian inference in latent Gaussian models. INLA has the advantage of giving very accurate approximations and being faster than MCMC methods when the number of hyperparameters does not exceed 6 (as in our case). Model comparison is performed using the Deviance Information Criterion.

The skew-normal model is a class of distributions that extends the Gaussian family by including a skewness parameter. This model presents some inferential problems linked to the estimation of the skewness parameter. In particular its maximum likelihood estimator can be infinite especially for moderate sample sizes and is not clear how to calculate confidence intervals for this parameter. In this work, we show how these inferential problems can be solved if we are interested in the distribution of extreme statistics of two random variables with joint normal distribution. Such situations are not uncommon in applications, especially in medical and environmental contexts, where it can be relevant to estimate the distribution of extreme statistics. A theoretical result, found by Loperfido [7], proves that such extreme statistics have a skew-normal distribution with skewness parameter that can be expressed as a function of the correlation coefficient between the two initial variables. It is then possible, using...