Efficacy of needle-placement technique in radiofrequency ablation for treatment of lumbar facet arthropathy.

BACKGROUND:Many studies have assessed the efficacy of radiofrequency ablation to denervate the facet joint as an interventional means of treating axial low-back pain. In these studies, varying procedural techniques were utilized to ablate the nerves that innervate the facet joints. To date, no comparison studies have been performed to suggest superiority of one technique or even compare the prevalence of side effects and complications. MATERIALS AND METHODS:A retrospective chart review was performed on patients who underwent a lumbar facet denervation procedure. Each patient's chart was analyzed for treatment technique (early versus advanced Australian), preprocedural visual numeric scale (VNS) score, postprocedural VNS score, duration of pain relief, and complications. RESULTS:Pre- and postprocedural VNS scores and change in VNS score between the two groups showed no significant differences. Patient-reported benefit and duration of relief was greater in the advanced Australian technique group (P=0.012 and 0.022, respectively). The advanced Australian technique group demonstrated a significantly greater median duration of relief (4 months versus 1.5 months, P=0.022). Male sex and no pain-medication use at baseline were associated with decreased postablation VNS scores, while increasing age and higher preablation VNS scores were associated with increased postablation VNS scores. Despite increasing age being associated with increased postablation VNS scores, age and the advanced Australian technique were found to confer greater patient self-reported treatment benefit. CONCLUSION:The advanced Australian technique provides a significant benefit over the early Australian technique for the treatment of lumbar facet pain, both in magnitude and duration of pain relief

4Ward: a Relayering Strategy for Efficient Training of Arbitrarily Complex Directed Acyclic Graphs

Thanks to their ease of implementation, multilayer perceptrons (MLPs) have become ubiquitous in deep learning applications. The graph underlying an MLP is indeed multipartite, i.e. each layer of neurons only connects to neurons belonging to the adjacent layer. In contrast, in vivo brain connectomes at the level of individual synapses suggest that biological neuronal networks are characterized by scale-free degree distributions or exponentially truncated power law strength distributions, hinting at potentially novel avenues for the exploitation of evolution-derived neuronal networks. In this paper, we present ``4Ward'', a method and Python library capable of generating flexible and efficient neural networks (NNs) from arbitrarily complex directed acyclic graphs. 4Ward is inspired by layering algorithms drawn from the graph drawing discipline to implement efficient forward passes, and provides significant time gains in computational experiments with various Erd\H{o}s-R\'enyi graphs. 4Ward not only overcomes the sequential nature of the learning matrix method, by parallelizing the computation of activations, but also addresses the scalability issues encountered in the current state-of-the-art and provides the designer with freedom to customize weight initialization and activation functions. Our algorithm can be of aid for any investigator seeking to exploit complex topologies in a NN design framework at the microscale

Beyond Multilayer Perceptrons: Investigating Complex Topologies in Neural Networks

In this study, we explore the impact of network topology on the approximation capabilities of artificial neural networks (ANNs), with a particular focus on complex topologies. We propose a novel methodology for constructing complex ANNs based on various topologies, including Barab\'asi-Albert, Erd\H{o}s-R\'enyi, Watts-Strogatz, and multilayer perceptrons (MLPs). The constructed networks are evaluated on synthetic datasets generated from manifold learning generators, with varying levels of task difficulty and noise. Our findings reveal that complex topologies lead to superior performance in high-difficulty regimes compared to traditional MLPs. This performance advantage is attributed to the ability of complex networks to exploit the compositionality of the underlying target function. However, this benefit comes at the cost of increased forward-pass computation time and reduced robustness to graph damage. Additionally, we investigate the relationship between various topological attributes and model performance. Our analysis shows that no single attribute can account for the observed performance differences, suggesting that the influence of network topology on approximation capabilities may be more intricate than a simple correlation with individual topological attributes. Our study sheds light on the potential of complex topologies for enhancing the performance of ANNs and provides a foundation for future research exploring the interplay between multiple topological attributes and their impact on model performance

Sobre el efecto de los plaguicidas en la Cuenca del Salado de la Provincia de Buenos Aires

El río Salado de la Provincia de Buenos Aires se caracteriza por su régimen de alimentación por aguas subterráneas. Esto, sumado a particularidades de las precipitaciones y de la permeabilidad de los suelos, da origen a tierras anegadas de permanencia prolongada. Por tratarse de una zona agropecuaria, es evidente la utilización de plaguicidas para mejorar el rendimiento de cultivos y ganado. Durante el período de inundación estos agroquímicos se expanden por los suelos y posteriormente se infiltran en las napas, siendo fuente de consumo de la población, en especial de la rural, que llega a los niveles máximos de mortalidad de la provincia de Buenos Aires. Esta circunstancia, además, altera los suelos desertificándolos y destruye la fauna, en especial, la ictícola. El objetivo de este trabajo es la elaboración de mapas de riesgo, solicitados por el Ministerio de Salud y Acción Social de la Nación

Predicting the bearing capacity of road flexible pavements using GPR

Most of the damage in road-flexible pavements occur where stiffness of the asphalt and loadbearing layers is low. To this extent, an effective assessment of the strength and deformation properties of these layers can help to identify the most critical sections [1]. This work proposes an experimental-based model [2] for the assessment of the bearing capacity of road-flexible pavements using ground-penetrating radar (GPR – 2 GHz horn antenna) and the Curviameter [3] non-destructive testing (NDT) methods. It is known that the identification of early decay and loss of bearing capacity is a major challenge for effective maintenance of roads and the implementation of pavement management systems (PMSs). To this effect, a time-efficient methodology based on a quantitative modelling of road bearing capacity is developed in this study. The viability of using a GPR system in combination with the Curviameter NDT equipment is also proven. The research is supported by the Italian Ministry of Education, University and Research under the National Project “Extended resilience analysis of transport networks (EXTRA TN): Towards a simultaneously space, aerial and ground sensed infrastructure for risks prevention”, PRIN 2017, Prot. 20179BP4S

An investigation into the railway ballast grading using GPR and image analysis

This study reports on an investigation into the grain size distribution of the railway ballast using ground-penetrating radar (GPR) and image analysis. The proposed approach relies on the hypothesis that the dimension (grading) of the ballast aggregates can influence the back-reflected spectrum received by the use of GPR. This assumption was confirmed by the finite difference time-domain (FDTD) simulations of the GPR signal, which were run by using the numerical simulator package gprMax 2D. A regression model was developed which related the "equivalent" diameter of the ballast aggregates and the frequency of the peak within the received spectrum. The model was validated in the laboratory environment by means of a 155 cm x 155 cm x 50 cm methacrylate tank, filled up with railway ballast. An air-coupled GPR system equipped with a 2000 MHz central frequency antenna was used for testing purposes. A total of three spatial distributions of the ballast aggregates within the tank were investigated, by emptying out and filling up thrice the tank with the same material. The geometric information on the ballast grading obtained from the simulation-based regression model was compared to the actual grading curve of the ballast. To this effect, an algorithm based on the automatic image analysis was developed. The comparison showed that the modelled aggregate diameter corresponded to the 70 % of the grading of the material sieved out in the laboratory. This contribution paves the way to new methodologies for the non-destructive assessment and the monitoring of segregation phenomena within the railway ballast layers in railway track-beds

Lawfare, medios y democracia: análisis de tapas del diario Clarín y La Nación

Este trabajo busca analizar las estrategias y métodos empleados por dos medios de comunicación, en sus versiones gráficas impresas, específicamente los titulares publicados por La Nación y Diario Clarín. Se tomarán como objeto empírico los encabezados publicados durante los meses de enero, febrero y marzo del año 2021, en el marco de la construcción de un conflicto político entre el Poder Ejecutivo y el Poder Judicial del Estado argentino. Entendemos que la relación entre los medios de comunicación, el sistema judicial y los sectores de poder en América Latina han evidenciado acciones en las que se debilita y pone en juego el sistema democrático y en este sentido resulta relevante evidenciar los modos en que los medios de comunicación construyen escenarios de inestabilidad y conflicto social