Interlayer Shear Failure Evolution with Different Test Equipments

AbstractThis research analyses the relationship between several configurations, failure mechanisms and states of stress imposed by testing machines, comparing the results of two devices. It focalizes on the evolution of numerous tests, performed on identical specimens, and reports the correspondent response curves obtained with two devices suitably designed to cover two kind of devices used in recent years and modified to ensure the comparison between the outcomes.The observation of a regular trend in the results suggests a strict relationship between them and the specimens’ features and also warrants the statistic reliability of the testing machines

Spectroradiometric Laboratory Measures on Asphalt Concrete: Preliminary Results

AbstractThis paper presents the preliminary results of a project concerning the use of spectroradiometric measurements for the characterization of aggregates and asphalts mixtures commonly used for road paving.Radiometrical measurements, in the wavelength range between 350-2500nm, were performed on selected samples with different compositional characteristics; the relationships between spectral signatures and different bituminous mixtures samples were analyzed.The results suggest that spectroradiometrics analyses can be used to establish new efficient and fast road classification procedures to support activities of pavement management systems and interpretation of remote sensed images

Predicting Fishing Footprint of Trawlers From Environmental and Fleet Data: An Application of Artificial Neural Networks

The increasing use of tracking devices, such as the Vessel Monitoring System (VMS) and the Automatic Identification System (AIS), have allowed, in the last decade, detailed spatial and temporal analyses of fishing footprints and of their effects on environments and resources. Nevertheless, tracking devices usually allow monitoring of the largest length classes composing different fleets, whereas fishing vessels below a regulatory threshold (i.e., 15 m in length-over-all) are not mandatorily equipped with these tools. This issue is critical, since 36% of the vessels in the European Union (EU) fleets belong to these “hidden” length classes. In this study, a model [namely, a cascaded multilayer perceptron network (CMPN)] is devised to predict the annual fishing footprints of vessels without tracking devices. This model uses information about fleet structures, environmental characteristics, human activities, and fishing effort patterns of vessels equipped with tracking devices. Furthermore, the model is able to take into account the interactions between different components of the fleets (e.g., fleet segments), which are characterized by different operating ranges and compete for the same marine space. The model shows good predictive performance and allows the extension of spatial analyses of fishing footprints to the relevant, although still unexplored, fleet segments

Are public health professionals prepared for public health genomics? A cross-sectional survey in Italy

Background: Public health genomics is an emerging multidisciplinary approach, which aims to integrate genome-based knowledge in a responsible and effective way into public health. Despite several surveys performed to evaluate knowledge, attitudes and professional behaviors of physicians towards predictive genetic testing, similar surveys have not been carried out for public health practitioners. This study is the first to assess knowledge, attitudes and training needs of public health professionals in the field of predictive genetic testing for chronic diseases. Methods: A self-administered questionnaire was used to carry out a cross-sectional survey of a random sample of Italian public health professionals. Results: A response rate of 67.4% (797 questionnaires) was achieved. Italian public health professionals have the necessary attitudinal background to contribute to the proper use of predictive genetic testing for chronic diseases, but they need additional training to increase their methodological knowledge. Knowledge significantly increases with exposure to predictive genetic testing during postgraduate training (odds ratio (OR) = 1.74, 95% confidence interval (CI) = 1.05-2.88), time dedicated to continuing medical education (OR = 1.53, 95% CI = 1.14-2.04) and level of English language knowledge (OR = 1.36, 95% CI = 1.07-1.72). Adequate knowledge is the strongest predictor of positive attitudes from a public health perspective (OR = 3.98, 95% CI = 2.44-6.50). Physicians show a lower level of knowledge and more public health attitudes than other public health professionals do. About 80% of public health professionals considered their knowledge inadequate and 86.0% believed that it should be improved through specific postgraduate training courses. Conclusions: Specific and targeted training initiatives are needed to develop a skilled public health workforce competent in identifying genomic technology that is ready for use in population health and in modeling public health genomic programs and primary care services that need to be developed, implemented and evaluated

A Robotized Raspberry-Based System for Pothole 3D Reconstruction and Mapping

Repairing potholes is a task for municipalities to prevent serious road user injuries and vehicle damage. This study presents a low-cost, high-performance pothole monitoring system to maintain urban roads. The authors developed a methodology based on photogrammetry techniques to predict the pothole's shape and volume. A collection of overlapping 2D images shot by a Raspberry Pi Camera Module 3 connected to a Raspberry Pi 4 Model B has been used to create a pothole 3D model. The Raspberry-based configuration has been mounted on an autonomous and remote-controlled robot (developed in the InfraROB European project) to reduce workers' exposure to live traffic in survey activities and automate the process. The outputs of photogrammetry processing software have been validated through laboratory tests set as ground truth; the trial has been conducted on a tile made of asphalt mixture, reproducing a real pothole. Global Positioning System (GPS) and Geographical Information System (GIS) technologies allowed visualising potholes on a map with information about their centre, volume, backfill material, and an associated image. Ten on-site tests validated that the system works in an uncontrolled environment and not only in the laboratory. The results showed that the system is a valuable tool for monitoring road potholes taking into account construction workers' and road users' health and safety

The multi-stakeholder dialogue linked to the implementation of Professionalization Practices in the IT sector of higher technical institutes (Corrientes, Argentina)

El artículo se orienta a analizar las tensiones y posibles articulaciones que se generan a partir dela implementación de las Prácticas Profesionalizantes (PP) en el sector informática de institutos superiores técnicos de Corrientes (Argentina). A partir de la Ley de Educación Técnico Profesional del país, las PP constituyen un espacio formativo innovador dentro del sistema; al mismo tiempo, emergen como un ámbito para el desarrollo de las alianzas y vinculaciones basadas en el diálogo y concertación entre actores educativos y los del ámbito productivo. En el marco de una mesa de trabajo de la que participaron funcionarios y técnicos del Ministerio de Educación, directivos y profesores de institutos técnicos del sector y el representante de la cámara empresarial Polo IT, identificamos tensiones entre los discursos de los actores del sistema educativo y los del productivo. Las tensiones dan cuenta de modos diferentes de concebir y pensar cuestiones vinculadas a las PP, su régimen y posibilidades de implementación, los perfiles y saberes para la actividad profesional.The article is oriented to analyze the tensions and possible articulations that are generated from the implementation of the Professionalizing Practices (PP) in the informatics sector of higher technical institutes of Corrientes (Argentina). Based on the country’s Professional Technical Education Law, the PP constitute an innovative training space within the system, at the same time emerging as an area for the development of alliances and linkages based on dialogue and agreement between educational actors and of the productive environment. Within the framework of a working table in which officials and technicians from the Ministry of Education, managers and professors from technical institutes in the sector and the representative of the Polo IT business chamber participated, we identified tensions between the speeches of the actors in the educational system and those of the productive system. The tensions account for different ways of conceiving and thinking about issues related to PP, their regime and possibilities of implementation, the profiles and knowledge for the professional activity.Fil: D’Andrea, Ana Maria. Universidad Nacional del Nordeste. Facultad de Humanidades; ArgentinaFil: Bountempo, María Paula. Universidad Nacional del Nordeste. Facultad de Humanidades; ArgentinaFil: Pozzer Pianalto, José Antonio. Universidad Nacional del Nordeste; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentin

Time- and frequency-resolved fluorescence with a single TCSPC detector via a Fourier-transform approach

We introduce a broadband single-pixel spectro-temporal fluorescence detector, combining time-correlated single photon counting (TCSPC) with Fourier transform (FT) spectroscopy. A birefringent common-path interferometer (CPI) generates two time-delayed replicas of the sample’s fluorescence. Via FT of their interference signal at the detector, we obtain a two-dimensional map of the fluorescence as a function of detection wavelength and emission time, with high temporal and spectral resolution. Our instrument is remarkably simple, as it only requires the addition of a CPI to a standard single-pixel TCSPC system, and it shows a readily adjustable spectral resolution with inherently broad bandwidth coverag

Time domain diffuse Raman spectroscopy using single pixel detection

: Diffuse Raman spectroscopy (DIRS) extends the high chemical specificity of Raman scattering to in-depth investigation of thick biological tissues. We present here a novel approach for time-domain diffuse Raman spectroscopy (TD-DIRS) based on a single-pixel detector and a digital micromirror device (DMD) within an imaging spectrometer for wavelength encoding. This overcomes the intrinsic complexity and high cost of detection arrays with ps-resolving time capability. Unlike spatially offset Raman spectroscopy (SORS) or frequency offset Raman spectroscopy (FORS), TD-DIRS exploits the time-of-flight distribution of photons to probe the depth of the Raman signal at a single wavelength with a single source-detector separation. We validated the system using a bilayer tissue-bone mimicking phantom composed of a 1 cm thick slab of silicone overlaying a calcium carbonate specimen and demonstrated a high differentiation of the two Raman signals. We reconstructed the Raman spectra of the two layers, offering the potential for improved and quantitative material analysis. Using a bilayer phantom made of porcine muscle and calcium carbonate, we proved that our system can retrieve Raman peaks even in the presence of autofluorescence typical of biomedical tissues. Overall, our novel TD-DIRS setup proposes a cost-effective and high-performance approach for in-depth Raman spectroscopy in diffusive media

ECLAC: Brazil, Argentina and the Problems and Mistakes in the Latin American Structuralist Economic Theory

To a large part, Latin American economic thought is rooted in the view that the State should be the engine of economic development. The theory developed by the Latin American Structuralist School supports this view. ECLAC, a United Na-tions development commission, is the bastion of the policy agenda branch of structuralism. Despite their central role in Latin American economic policies, structuralist ideas have hardly been discussed. We trace the historical origins of this school and use the theoretical standpoint of the Austrian School of Eco-nomics to talk about its views and mistakes

Overcoming treatment-resistant depression with machine-learning based tools: a study protocol combining EEG and clinical data to personalize glutamatergic and brain stimulation interventions (SelecTool Project)

© 2024 The Authors. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). https://creativecommons.org/licenses/by/4.0/Treatment-Resistant Depression (TRD) poses a substantial health and economic challenge, persisting as a major concern despite decades of extensive research into novel treatment modalities. The considerable heterogeneity in TRD’s clinical manifestations and neurobiological bases has complicated efforts toward effective interventions. Recognizing the need for precise biomarkers to guide treatment choices in TRD, herein we introduce the SelecTool Project. This initiative focuses on developing (WorkPlane 1/WP1) and conducting preliminary validation (WorkPlane 2/WP2) of a computational tool (SelecTool) that integrates clinical data, neurophysiological (EEG) and peripheral (blood sample) biomarkers through a machine-learning framework designed to optimize TRD treatment protocols. The SelecTool project aims to enhance clinical decision-making by enabling the selection of personalized interventions. It leverages multi-modal data analysis to navigate treatment choices towards two validated therapeutic options for TRD: esketamine nasal spray (ESK-NS) and accelerated repetitive Transcranial Magnetic Stimulation (arTMS). In WP1, 100 subjects with TRD will be randomized to receive either ESK-NS or arTMS, with comprehensive evaluations encompassing neurophysiological (EEG), clinical (psychometric scales), and peripheral (blood samples) assessments both at baseline (T0) and one month post-treatment initiation (T1). WP2 will utilize the data collected in WP1 to train the SelecTool algorithm, followed by its application in a second, out-of-sample cohort of 20 TRD subjects, assigning treatments based on the tool’s recommendations. Ultimately, this research seeks to revolutionize the treatment of TRD by employing advanced machine learning strategies and thorough data analysis, aimed at unraveling the complex neurobiological landscape of depression. This effort is expected to provide pivotal insights that will promote the development of more effective and individually tailored treatment strategies, thus addressing a significant void in current TRD management and potentially reducing its profound societal and economic burdens.Peer reviewe