Attention-Deficit Hyperactivity Disorder: Teachers\u27 Perceptions and Acceptability of Interventions

The purpose of this study was to examine elementary and middle school teachers’ perceptions of attention deficit/ hyperactivity disorder (ADHD) and acceptability of interventions commonly used in the treatment of ADHD. Eighty-one teachers from three elementary schools and one middle school participated in this study by completing an online survey containing the Perception of Attention Deficit Disorder Survey (PADDS) and Intervention Acceptability Survey (IAS). Results indicate that teachers feel adequately trained on the topic of ADHD and feel confident when implementing interventions for students with ADHD; however, teachers would like to receive additional in-service training on the topic of ADHD. Teachers perceive students with hyperactiveimpulsive symptoms of ADHD to be more difficult to manage in comparison to students with predominantly inattentive symptoms of ADHD. Medication and positive behavioral interventions were viewed as equally favorable in the treatment of the inattentive symptoms of ADHD by teachers; however, medication was rated more favorably in the treatment of the combined (inattentive and hyperactive-impulsive) symptoms of ADHD. Large class size and lack of staff support were identified as barriers in intervention implementation, with large class size being identified as the greatest barrier. Based on this information, school psychologists and other service providers who suggest interventions for teachers to use for students with ADHD need to consider the factors that contribute to teachers’ perceptions and acceptability of interventions

Simple absorbing layer conditions for shallow wave simulations with Smoothed Particle Hydrodynamics

We study and implement a simple method, based on the Perfectly Matched Layer approach, to treat non reflecting boundary conditions with the Smoothed Particles Hydrodynamics numerical algorithm. The method is based on the concept of physical damping operating on a fictitious layer added to the computational domain. The method works for both 1D and 2D cases, but here we illustrate it in the case of 1D and 2D time dependent shallow waves propagating in a finite domain

Nonlinear Loan Loss Provisioning

The extant banking literature often models loan loss provisions as a linear function of changes in loan portfolio quality. Large sample data indicate that this linearity assumption is invalid and that a V-shaped piecewise linear specification fits much better. Decreases in nonperforming loans are associated with increases in loan loss provisions. This anomalous asymmetric relation is partly driven by the mechanical accounting effects of loan charge-offs on nonperforming loans and allowance for loan losses. We find that, controlling for concurrent loan charge-offs, loan loss provisions move in the same direction as nonperforming loan change, but asymmetry remains. The effect of nonperforming loan increases on loan loss provisions is still twice as large as that of nonperforming loan decreases. We argue that the residual asymmetry is caused by conditional conservatism. We show that loan loss provision asymmetry is greater for banks with more high-risk construction loans and shorter-maturity loans and for public banks, and is more pronounced during economic downturns and in the fourth quarter, consistent with the predictable effects of conditional conservatism

Quasivariational Inequalities for a Dynamic Competitive Economic Equilibrium Problem

The aim of this paper is to consider a dynamic competitive economic equilibrium problem in terms of maximization of utility functions and of excess demand functions. This equilibrium problem is studied by means of a time-dependent quasivariational inequality which is set in the Lebesgue space . This approach allows us to obtain an existence result of time-dependent equilibrium solutions

A multi-modal machine learning approach to detect extreme rainfall events in Sicily

In 2021 almost 300 mm of rain, nearly half of the average annual rainfall, fell near Catania (Sicily Island, Italy). Such events took place in just a few hours, with dramatic consequences on the environmental, social, economic, and health systems of the region. These phenomena are now very common in various countries all around the world: this is the reason why, detecting local extreme rainfall events is a crucial prerequisite for planning actions, able to reverse possibly intensified dramatic future scenarios. In this paper, the Affinity Propagation algorithm, a clustering algorithm grounded on machine learning, was applied, to the best of our knowledge, for the first time, to detect extreme rainfall areas in Sicily. This was possible by using a high-frequency, large dataset we collected, ranging from 2009 to 2021 which we named RSE (the Rainfall Sicily Extreme dataset). Weather indicators were then been employed to validate the results, thus confirming the presence of recent anomalous rainfall events in eastern Sicily. We believe that easy-to-use and multi-modal data science techniques, such as the one proposed in this study, could give rise to significant improvements in policy-making for successfully contrasting climate change

Emergence of Diversity in a Group of Identical Bio-Robots

Learning capabilities, often guided by competition/cooperation, play a fundamental and ubiquitous role in living beings. Moreover, several behaviours, such as feeding and courtship, involve environmental exploration and exploitation, including local competition, and lead to a global benefit for the colony. This can be considered as a form of global cooperation, even if the individual agent is not aware of the overall effect. This paper aims to demonstrate that identical biorobots, endowed with simple neural controllers, can evolve diversified behaviours and roles when competing for the same resources in the same arena. These behaviours also produce a benefit in terms of time and energy spent by the whole group. The robots are tasked with a classical foraging task structured through the cyclic activation of resources. The result is that each individual robot, while competing to reach the maximum number of available targets, tends to prefer a specific sequence of subtasks. This indirectly leads to the global result of task partitioning, whereby the cumulative energy spent, in terms of the overall travelled distance and the time needed to complete the task, tends to be minimized. A series of simulation experiments is conducted using different numbers of robots and scenarios: the common emergent result obtained is the role specialization of each robot. The description of the neural controller and the specialization mechanisms are reported in detail and discussed