4,268 research outputs found
Criticalities in applying the Neyman’s optimality in business surveys : a comparison of selected allocation methods
No full textIn finite population sampling, when dealing with negligible sampling fractions (budget
constraints) or when data quality is not satisfactory (e.g. frame lists, auxiliary information,
etc.), a rethinking of Tchuprow’s (1924) optimality concept (later extended by Neyman, 1934)
in stratified sampling is needed. In fact, the need of stratum representativeness from one side
and the optimum allocation from the other are often in conflict. Furthermore, the choice of a
sampling design which is rigorously respectful of the statistical theory is a difficult task in
practice, especially when knowledge barriers and operational constraints are present.
Sometimes a purposive design is the only possibility. This work aims at finding an optimal
allocation method from a population of enterprises. This task is carried out through a
simulation approach which compares different methodologies. We consider, among others,
and together with the popular optimal allocation and its multivariate extension (Bethel’s
algorithm -1989), the ‘Optimum Robust Allocation with Uniform Stratum Threshold –
AORSU’ (Chiodini P.M., Manzi G., Verrecchia F.; 2008) and the ‘dynamic allocation’
(Buisson, 2009). These methods are suitable both for domain analyses and for the
improvement of the estimates, obtained through a proxy of the stratum variability. In general,
AORSU is useful when an ex-ante allocation is possible. On the other hand the dynamic
allocation (i.e. ex-post) is becoming more and more of interest especially when no
information about stratum variability is available. The method relies on an in itinere adjustment process (i.e. an allocation based upon an estimate of the sampling variance of the first sampling data from the strata). For the latter method, even though a number of practical
solutions for the survey list are available (see, e.g., Chiodini P.M., Facchinetti, Manzi G., Nai Ruscone M., Verrecchia F, 2008), the substitution of sampling units to be interviewed, which is routinely performed, is the real practical drawback, especially when organizational problems, related to target shifting, arise. The solution to this is generally not trivial.
Simulations will be carried out on different databases (ASIA - ISTAT; Italian Register of
enterprises - Chamber of Commerce of Milan, Italy
Criticalities in applying the Neyman’s optimality in business surveys : a comparison of selected allocation methods
No full textIn finite population sampling, when dealing with negligible sampling fractions (budget
constraints) or when data quality is not satisfactory (e.g. frame lists, auxiliary information,
etc.), a rethinking of Tchuprow’s (1924) optimality concept (later extended by Neyman, 1934)
in stratified sampling is needed. In fact, the need of stratum representativeness from one side
and the optimum allocation from the other are often in conflict. Furthermore, the choice of a
sampling design which is rigorously respectful of the statistical theory is a difficult task in
practice, especially when knowledge barriers and operational constraints are present.
Sometimes a purposive design is the only possibility. This work aims at finding an optimal
allocation method from a population of enterprises. This task is carried out through a
simulation approach which compares different methodologies. We consider, among others,
and together with the popular optimal allocation and its multivariate extension (Bethel’s
algorithm -1989), the ‘Optimum Robust Allocation with Uniform Stratum Threshold –
AORSU’ (Chiodini P.M., Manzi G., Verrecchia F.; 2008) and the ‘dynamic allocation’
(Buisson, 2009). These methods are suitable both for domain analyses and for the
improvement of the estimates, obtained through a proxy of the stratum variability. In general,
AORSU is useful when an ex-ante allocation is possible. On the other hand the dynamic
allocation (i.e. ex-post) is becoming more and more of interest especially when no
information about stratum variability is available. The method relies on an in itinere adjustment process (i.e. an allocation based upon an estimate of the sampling variance of the first sampling data from the strata). For the latter method, even though a number of practical
solutions for the survey list are available (see, e.g., Chiodini P.M., Facchinetti, Manzi G., Nai Ruscone M., Verrecchia F, 2008), the substitution of sampling units to be interviewed, which is routinely performed, is the real practical drawback, especially when organizational problems, related to target shifting, arise. The solution to this is generally not trivial.
Simulations will be carried out on different databases (ASIA - ISTAT; Italian Register of
enterprises - Chamber of Commerce of Milan, Italy
VOLCANIC CO2 FLUX MEASUREMENTS BY TUNABLE DIODE LASER ABSORPTION SPECTROSCOPY
No full textIntroduction
In the last decades, the use of near-infrared room-temperature diode lasers for gas sensing has grown significantly. The use of these devices, for instance in combination with optical fibers, is particularly convenient for volcanic monitoring applications [1,2]. Here, we report on the first results of the application of an open-path infrared tunable laser-based at Campi Flegrei (Southern Italy). Such Diode-laser-based measurements were performed, during two field campaigns (october 2012, and january 2013), in the attempt to obtain novel information on the current degassing unrest of Solfatara and Pisciarelli fumarolic fields.
Results and Discussion
At each site, we used an ad-hoc designed measurement geometry, using a TDLS (a Gas Finder unit) and several differently positioned retroreflectors (mirrors), to scan the fumaroles’ plume from different angles and distances. From post-processing of the data (acquired at 1 hz), we derived tomographic maps of CO2 concentrations in the plume and, by integration and combination with plume transport speed (from video cameras), we inferred the CO2 flux directly. The so-calculated fluxes, the first ever obtained at Campi Flegrei, average of 500 tons/day, and support a significant contribution of fumaroles to the total CO2 budget. The cumulative (fumarole [this study] +soil [3]) CO2 output from Campi Flegrei is finally evaluated at 1600 tons/day.
[1] Gianfrani L. et al. (2000). Appl. Phys. B-Rapid Common. 70, 467-470. [2] Richter D. et al (2002), Optics and Lasers in Engineering, Volume 37, Issues 2–3, Pages 171-186. [3] Chiodini G. et al. (2010), Journal of Geophysical Research, Volume 115, B03205, doi:10.1029/2008JB006258
Criticalities in applying the Neyman's optimality in business surveys : a comparison of selected allocation methods
No full textIn finite population sampling, when dealing with negligible sampling fractions (budget constraints) or when data quality is not
satisfactory (e.g. frame lists, auxiliary information, etc.), a rethinking of Tchuprow’s (1924) optimality concept (later extended by Neyman, 1934) in stratified sampling is needed. In fact, the need of stratum representativeness from one side and the optimum allocation from the other
are often in conflict. Furthermore, the choice of a sampling design which is rigorously respectful of the statistical theory is a difficult task in practice, especially when knowledge barriers and operational constraints are present. Sometimes a purposive design is the only possibility.
This work aims at finding an optimal allocation method from a population of enterprises. This task is carried out through a simulation approach which compares different methodologies. We consider, among others, and together with the popular optimal allocation and its multivariate extension (Bethel’s algorithm -1989), the “Optimum Robust Allocation with Uniform Stratum Threshold - AORSU” (Chiodini P.M.,
Manzi G., Verrecchia F.; 2008) and the “dynamic allocation” (Buisson, 2009). These methods are suitable both for domain analyses and for the improvement of the estimates, obtained through a proxy of the stratum variability. In general, AORSU is useful when an ex-ante allocation is possible. On the other hand the dynamic allocation (i.e. ex-post) is becoming more and more of interest especially when no information about
stratum variability is available. The method relies on an in itinere adjustment process (i.e. an allocation based upon an estimate of the sampling variance of the first sampling data from the strata
Ce doped SiO<sub>2</sub> optical fibers for remote radiation sensing and measurement
No full textScintillating materials, able to efficiently operate the conversion of energy absorbed in the form of ionizing radiation into light in the visible UV interval, are presently used in a wide class of applications as medical imaging, industrial inspection, security controls and high energy physics detector. In the last few years we studied and developed a new radiation sensor based on silica-glass fiber-optic technology. In its simplest configuration such device is composed by a short portion (about 10 mm) of scintillating fiber coupled to a photomultiplier by a suitably long passive silica fiber.At the early stage of its market introduction it is the smallest radiation sensor, also with respect to MOSFET and diode technology and it appears to be the ideal choice for in vivo measurements in medical field or remote sensing
3D Radiometric Mapping by Means of LiDAR SLAM and Thermal Camera Data Fusion
No full textThe ability to produce 3D maps with infrared radiometric information is of great interest for many applications, such as rover navigation, industrial plant monitoring, and rescue robotics. In this paper, we present a system for large-scale thermal mapping based on IR thermal images and 3D LiDAR point cloud data fusion. The alignment between the point clouds and the thermal images is carried out using the extrinsic camera-to-LiDAR parameters, obtained by means of a dedicated calibration process. Rover’s trajectory, which is necessary for point cloud registration, is obtained by means of a LiDAR Simultaneous Localization and Mapping (SLAM) algorithm. Finally, the registered and merged thermal point clouds are represented through an OcTree data structure, where each voxel is associated with the average temperature of the 3D points contained within. Furthermore, the paper presents in detail the method for determining extrinsic parameters, which is based on the identification of a hot cardboard box. Both methods were validated in a laboratory environment and outdoors. It is shown that the developed system is capable of locating a thermal object with an accuracy of up to 9 cm in a 45 m map size with a voxelization of 14 cm
Participatory networks towards social change
No full textFoundation
The proposal is founded on the scientific contributions related to Participatory Network Analysis. It is aimed at analyzing the interest of not only international scholars but also community activists on this topic and discussing research and interventions for the enhancement of community psychology models towards collective processes of change. The multidisciplinary and fruitful proposals will be highlighted, as well as some critical aspects in defining the participatory approach. PNA has proven to be effective in many fields including health promotion; migrants’ integration; spaces for everyday life; policies; learning environments; disasters and critical events management (see: Chiodini, M., & Meringolo, P. (2022). Di cosa parliamo quando parliamo di Participatory network analysis?: una review sistematica. What do we talk about when we talk about Participatory Network Analysis? A systematic review. Psicologia di comunità, 1:11-42).
Particular attention will be paid to gender issues (and the feminist approach) and intersectional aspects of the topic.
Goals
The main goals will be to clarify the meaning of “Participatory network analysis”; explore the different approaches based on different (and often conflictual) theories of change; and propose instruments and tools for applying a real participatory approach, particularly in difficult situations (e.g. marginalized people; young people in jail or on probation...)
The nitrification inhibitor Vizura® reduces N2O emissions when added to digestate before injection under irrigated maize in the Po Valley (Northern Italy)
No full textThe agricultural area in the Po Valley is prone to high nitrous oxide (N2O) emissions as it is characterized by irrigated maize-based cropping systems, high amounts of nitrogen supplied, and elevated air temperature in summer. Here, two monitoring campaigns were carried out in maize fertilized with raw digestate in a randomized block design in 2016 and 2017 to test the effectiveness of the 3, 4 DMPP inhibitor Vizura® on reducing N2O-N emissions. Digestate was injected into 0.15 m soil depth at side-dressing (2016) and before sowing (2017). Non-steady state chambers were used to collect N2O-N air samples under zero N fertilization (N0), digestate (D), and digestate + Vizura® (V). Overall, emissions were significantly higher in the D treatment than in the V treatment in both 2016 and 2017. The emission factor (EF, %) of V was two and four times lower than the EF in D in 2016 and 2017, respectively. Peaks of NO3-N generally resulted in N2O-N emissions peaks, especially during rainfall or irrigation events. The water-filled pore space (WFPS, %) did not differ between treatments and was generally below 60%, suggesting that N2O-N emissions were mainly due to nitrification rather than denitrification
Small and medium-sized firms and the current financial and economic crisis: a statistical perspective
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