Zoekresultaten

Development of an improvisational music therapy intervention for young adults with depressive symptoms

Depression is a highly prevalent and seriously impairing disorder. Evidence suggests that music therapy can decrease depression, though the music therapy that is offered is often not clearly described in studies. The purpose of this study was to develop an improvisational music therapy intervention based on insights from theory, evidence and clinical practice for young adults with depressive symptoms. The Intervention Mapping method was used and resulted in (1) a model to explain how emotion dysregulation may affect depressive symptoms using the Component Process Model (CPM) as a theoretical framework; (2) a model to clarify as to how improvisational music therapy may change depressive symptoms using synchronisation and emotional resonance; (3) a prototype Emotion-regulating Improvisational Music Therapy for Preventing Depressive symptoms (EIMT-PD); (4) a ten-session improvisational music therapy manual aimed at improving emotion regulation and reducing depressive symptoms; (5) a program implementation plan; and (6) a summary of a multiple baseline study protocol to evaluate the effectiveness and principles of EIMT-PD. EIMT-PD, using synchronisation and emotional resonance may be a promising music therapy to improve emotion regulation and, in line with our expectations, reduce depressive symptoms. More research is needed to assess its effectiveness and principles.

Integrating ecological and socioeconomic criteria in a GIS-based multicriteria-multiobjective analysis to develop sustainable harvesting strategies for Mexican oregano Lippia graveolens Kunth, a non-timber forest product

Mexican oregano is a non-timber forest product harvested in natural vegetation and represents an important source of income for rural families. Recent reports have highlighted decreases in natural populations caused by increased harvest intensity. Oregano leaf harvesting is a complex problem, involving different components and views, and has a clear spatial dimension. We proposed an analytical framework based on multi-criteria-multi-objective analyses. GIS tools were used as the platform for managing, displaying and analyzing ecological and socioeconomic information from different sources in order to evaluate land suitability of three different management strategies for two competing land objectives: oregano Harvest and oregano Regeneration. The incorporation of environmental evaluation criteria in the analysis allowed the identification of new potential oregano harvesting areas which were neither reported by harvesters, nor registered during harvesting trips. Socio-economic criteria, such as land tenure, highlighted the fact that a substantial proportion of current oregano harvesting areas are located outside ejido limits resulting in potential conflicts for resource access. The proposed Balanced oregano management strategy, in which the same proportion of suitable area (50%) was assigned to both objectives, represents the most favorable management strategy. This option allows harvesters to continue earning an income from oregano leaf harvest; and at the same time helps in the selection of the best areas for oregano regeneration. It also represents a management strategy with a smaller impact on oregano populations and on the harvesters ́ income, as well as lower monitoring costs. The proposed analytical frame-work may contribute to advance the application of systematic approaches for solving decision-making problems in areas where oregano leaves and other NTFP are harvested.

Filorete flock

This essay is based on research promoted by INDIRE, Italian NationalInstitute for Documentation, Innovation and Educational Researchin Education, and is developed under the research on ‘Professionalnetworks, Educational models and School principal’s profile in Italy’. Onthe basis of observation and analysis of research data, a new theoryis assumed and new characteristics are defined, belonging to bothprofessional networks and educational models applied to all typesof professional networks. The characteristics so far identified are:plastic nature of networks, network punctuated equilibrium, networkconnectivity, emergent behavior and sociality of network members.It is also shown how the knowledge shared in a network materializes inEvents that produce Event Capital. The theory will be complemented byan experimentation phase.

Point Cloud Analysis of Railway Infrastructure: A Systematic Literature Review

Introducing NBEATSx to realized volatility forecasting

This paper investigates the application of neural basis expansion analysis with exogenous variables (NBEATSx) in the prediction of daily stock realized volatility for various time steps. It compares NBEATSx’s forecasting accuracy and robustness with several commonly used models, namely Long-Short Term Memory (LSTM) network, Temporal Neural Network (TCN), HAR, GARCH, and GJR-GARCH models. In this research, a total of six distinct stock indexes, three error measures, and four statistical tests are used, while three robustness tests are conducted to verify the outcomes of this paper. The findings of this research show that NBEATSx consistently yields statistically more accurate and robust forecasts than the other considered models. On average, NBEATSx generates forecasts that are respectively 13% and 8% more accurate for medium-term and long-term forecasting. Additionally, it produces forecasts that are respectively 43%, 60%, and 59% more robust for short-term, medium-term, and long-term forecasting. Yet, it should be noted that the superiority of NBEATSx in terms of forecast accuracy is not evident when applied to stock indexes from developing countries.

A novel loss function for neural network models exploring stock realized volatility using Wasserstein Distance

This study proposes a novel loss function for neural network models that explores the topological structure of stock realized volatility (RV) data by adding Wasserstein Distance (WD). The study shows that the proposed loss statistically significantly improves the forecast accuracy of neural network models for magnitude-dependent error measures, for example, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), but not necessarily for relative error measures, such as Quasi-likelihood (QLIKE). Additionally, this research provides user-friendly open-source code for researchers and practitioners to implement the proposed loss function efficiently and quickly.