35436 research outputs found
Sort by
Optimal Synergy between Azulenes and Acenes in Azuacenes with 6‑7‑5 Ring Topology
Get PDFAzuacenes, defined as azulene fused with acenes in a 6-7-5 ring topology and spanning lengths from 3 to 6 rings, have been synthesized using a new skeleton editing and [3 + 2] annulation synthesis protocol as a distinction regarding the procedures to obtain the 6-5-7 isomers. Comprehensive studies on ground-state and excited-state spectroscopy, electrochemical properties, chemical stability, and solid-state structure have been conducted to compare these azuacenes with acenes. For the same number of rings, we found that azuacenes improve the chemical stability of acenes (i.e., smaller diradical character) and their photophysical properties (i.e., anti-Kasha emissions and modulation of the energy and strength of the visible bands) but they reduce the transport features compared to those of acenes. Compared with azulene, azuacenes improve the performance of azulene in terms of electrical properties. Overall, the fusion of known polycyclic compounds, such as acene and azulene, produces new isomeric hybrid compounds with enhanced properties. Here, the resulting compounds turn out to conserve most of the unique properties of the two building blocks that we associate with the facility of π-delocalization of the positive charge of the azulene zwitterion over the acene fragment.Funding for open access charge: Universidad de Málaga / CBU
Enhancing financial time series forecasting through topological data analysis
Get PDFTopological data analysis (TDA) is increasingly acknowledged within financial markets for its capacity to manage
complexity and discern nuanced patterns and structures. It has been applied effectively to uncover intricate relationships
and capture non-linear dependencies inherent in market data. This manuscript presents a groundbreaking study that delves
into integrating features derived from TDA to improve the performance of forecasting models for univariate time series
prediction. The research specifically examines whether incorporating features extracted from TDA-such as entropy,
amplitude, and the number of points obtained from persistent diagrams can provide valuable supplementary information to
the baseline forecasting model. Thus, the aim is to determine if these TDA-derived features can boost forecasting accuracy
by offering additional insights that existing models might overlook. The N-BEATS model serves as the baseline forecasting model due to its robust generalization capabilities and flexibility in incorporating additional features into the model.
The proposed methodology is compared against a univariate N-BEATS model without additional features and other
strategies incorporating supplementary features such as temporal decomposition and time delay embeddings. The evaluation includes forecasting for six cryptocurrencies across four distinct time scenarios and four traditional financial
instruments across two scenarios each, resulting in 32 datasets. The results obtained were promising, as the proposed
method, N - BEATS +TDA, achieved the best results in mean performance and mean ranking for the three metrics considered
(MAPE, MAE, and RMSE). Significant differences were observed with the rest of the proposed methods using a significance level of a α = 0:10, highlighting the effectiveness of integrating TDA features to enhance forecasting models.Funding for open access charge: Universidad de Málaga / CBU
Measuring psychological flexibility and inflexibility: further psychometric evidence of the Multidimensional Psychological Flexibility Inventory short form (MPFI-24) in Spanish population
Get PDFThe Multidimensional Psychological Flexibility Inventory (MPFI) is a recently developed measure of psychological flexibility and inflexibility (PF and PI) based on the Hexaflex/Inflexahex Model. The aim of this article was to assess the factorial structure, reliability, and convergent and criterion validity of the MPFI-24 in a sample from Spain. The MPFI-24 was tested in a sample of 508 university students (Study 1). Items comprising the acceptance and Experiential Avoidance (EA) subscales were rewritten and tested in a sample of 406 university students (Study 2). Participants completed online cross-sectional surveys including the Spanish MPFI-24, and measures of PF, PI, and mental health. Confirmatory factor analysis showed a six-component first-order factor structure for PF and for PI. The MPFI-24 demonstrated acceptable internal consistency, temporal stability, and convergent and criterion validity. The MPFI-24 is a promising tool. Potential areas for improvement of the measure are discussed.Funding for open access publishing: Universidad de Málaga/CBUA. This study received Grants PRE2020-093204 and PID2019-106086RB-I00 funded by MCIN/AEI /10.13039/501100011033 by “ESF Investing in your future”, and the Regional Government of Andalusia (HUM-566).
Funding for open access charge: Universidad de Málaga / CBUA
Aerotainment – Toward a research agenda merging airports and theme parks in the experience economy
Get PDFIn 2023, air traffic reached 88.6 % of pre-pandemic (2019) levels (IATA, 2024), with global tourism cities as key beneficiaries
(Graham, 2023; Marchesani et al., 2023). Similarly, theme parks have been found to be a major attractor for urban destinations
globally (Liang & Li, 2023; Luo & Li, 2024) and catalysts for economic development (see Raihan et al., 2024). Research by Tan and
Huang (2020) has shown that, unlike other tourism destinations, theme park visitors have particular expectations, which tend to focus
on elements of existential authenticity, such as recreating socio-cultural experiences offered by foreign destinations, without the need
to travel to those destinations. In turn, a number of key global tourism cities host some of the world's most visited theme parks (see
Fig. 1). For instance, Disneyland Paris has become France's most popular tourist destination, ahead of some of the city's iconic at
tractions, including the Cathedral of Notre-Dame and the Eiffel Tower.
The importance of air transport has been researched extensively by tourism scholars (see, for instance, Papatheodorou, 2021),
including the links between air travel and urban tourism (Coppola et al., 2024) and visitor attractions such as museums (Florido Benítez, 2023). Visitorswho visit theme parks also may be interested in experiencing other nearby attractions. Yet, there is a gap in our
understanding of how airports and nearby theme parks can jointly deliver higher levels of competitiveness for urban destinations
within the context of the experience economy.
In order to address this knowledge gap, “aerotainment” is suggested here as a means of conceptualising a systems-based inter
pretation (Park et al., 2016) of the interaction between airports, tourism cities, theme parks and nearby visitor attractions within the
context of the experience economy. Local residents and destination management organisations (DMOs) are key to this ecosystemFunding for open access charge: Universidad de Málaga/CBU
How does innovation emerge in open platform ecosystems?
Get PDFhttps://openpolicyfinder.jisc.ac.uk/id/publication/7942New collaborative contexts like open platforms represent a growing area in which the institutions are questioned and, in some cases, challenged (de-institutionalized) as part of a process that drives market innovation, allowing for ecosystem self-adjustment. This research investigates the underlying processes of change and evolution linked to innovation in the arts crowdfunding ecosystem context from an institutional point of view and connects the de-institutionalization process with the capability of a system to rely on the actors to make it possible for the ecosystem to evolve and create new institutions as a result of the interplay and institutional work. This paper addresses a gap in innovation emergence in service ecosystems, shedding light on the role of the de-institutionalization process as a path for inducing disruption relying on actors’ co-creation to make the system adjust and evolve through the emergence of new network practice
Advanced radiomic prediction of osteoporosis in primary hyperparathyroidism: a machine learning‑based analysis of CT images
Get PDFThis study aims to assess the proficiency of supervised machine learning techniques in discriminating between normal and abnormal bone mineral density (BMD) by leveraging clinical features and texture analysis of spinal bone tissue in patients diagnosed with primary hyperparathyroidism (PHP). From a total of 219 patients diagnosed with PHP, the 58 who had undergone both DXA and abdominal CT scan were included in this study. BMD was assessed by quantifying the Hounsfield units (HU) and performing texture analysis on every CT scan. The first lumbar vertebral body texture features were extracted by using LifeX 7.3.0 software. Initial classification into normal or abnormal BMD was performed with different machine learning techniques by training a model with the variables obtained from the texture analysis. Differentiating osteopenia from osteoporosis was evaluated by creating two models, one including the variables obtained from the texture analysis and HU and another one which only included the HU. Their performance was evaluated in the validation and test groups by calculating the accuracy, precision, recall, F1 score, and AUC. Bayes demonstrated higher performance for discerning individuals with normal and abnormal BMD, with an AUC of 0.916. The results from the second analysis showed a better performance for the model including the variables obtained from the texture analysis compared to the model that was solely trained with the HU (AUC in the training group of 0.77 vs. 0.65 in the test groups, respectively). In conclusion, analysis of BMD obtained from abdominal CT scans including texture analysis provide a better classification of normal density, osteo-penia and osteoporosis in patients with PHP
Dose assessment in several accidental scenarios involving lithium leakage in three IFMIF-DONES lithium system rooms.
Get PDFThis study assesses the radiological risks from potential failures in the lithium system of the IFMIF-DONES
(International Fusion Materials Irradiation Facility - Demo Oriented NEutron Source) facility, focusing on critical components in the Lithium Loop Cell (LLC), Hot Trap (H-trap), and Cold Trap (C-trap) rooms. Seven lithium leak scenarios were analyzed: four in the LLC room (near the electromagnetic pump, Target Lithium TLIC inlet/outlet, and primary heat exchanger), one in the H-trap, and one in the C-trap. Lithium release volumes varied from 0.017 m3 to 3.8 m3. Ambient dose equivalent rates, H*(10), were calculated using the MCNP 5.1.40 radiation transport code for gamma-emitting radionuclides like 7Be and activation products. Simulations included structural features affecting gamma transport, and dose maps were generated at various heights and distances from leaks. The most severe radiological conditions arose from the Primary Heat Exchanger (PHX) rupture and Ctrap leakage, with peak H*(10) rates of 94 mSv/h and 130 mSv/h, respectively. To meet annual dose limits (50 mSv/year), maximum allowable human intervention times ranged from 32 min (PHX rupture) to 714 min (Htrap failure). Post-leak access to the C-trap room is prohibited due to exceeding red zone thresholds. These results are essential for safety planning, remote handling, and accident mitigation strategies within the IFMIF-DONES lithium loop systems
Regulación emocional y suicidio: el papel de las creencias emocionales de incontrolabilidad.
Get PDFLa regulación emocional y el Síndrome de Crisis Suicida (SCS) son procesos psicológicos fundamentales en la conducta suicida. Dentro de la regulación emocional, la rumiación—un patrón de pensamiento repetitivo y pasivo centrado en los problemas—y las creencias disfuncionales sobre las emociones—relacionadas con expectativas, actitudes y percepciones sobre la naturaleza y el control de las emociones—se han vinculado con el suicidio. En particular, la rumiación se asocia con un mayor riesgo suicida, mientras que las creencias emocionales, especialmente aquellas sobre la incontrolabilidad de las emociones, aunque menos investigadas, también parecen estar relacionadas con la conducta suicida. Por su parte, el SCS es un estado mental pre-suicida caracterizado por una intensa desregulación cognitiva y afectiva, lo que lo convierte en un factor clave para la identificación del riesgo de suicidio a corto plazo.
En este contexto, esta tesis doctoral analiza la relación entre estas variables vinculadas a la desregulación emocional—con especial atención a las creencias de incontrolabilidad y la rumiación—y otros factores estrechamente asociados con la conducta suicida, como el SCS, las necesidades interpersonales (percepción de ser una carga para los demás y el sentimiento de pertenencia frustrada) y la propia conducta suicida (ideación e intentos de suicidio).
Para ello, se llevaron a cabo tres estudios con distintos enfoques metodológicos. En primer lugar, se realizó una revisión sistemática de la literatura para sintetizar los hallazgos previos sobre la relación entre la regulación emocional y la conducta suicida. Posteriormente, un estudio transversal con población comunitaria y clínica permitió analizar las relaciones entre las creencias de incontrolabilidad, la rumiación y el SCS en la conducta suicida en ambos grupos.Finalmente, un estudio longitudinal evaluó la capacidad predictiva de las creencias de incontrolabilidad y las necesidades interpersonales en el SCS y la ideación suicida a lo largo del tiempo. Los resultados obtenidos muestran que las dificultades en la regulación emocional están asociadas con la ideación e intento de suicidio en adultos y adolescentes, tanto en población clínica como comunitaria. En particular, la rumiación y las creencias de incontrolabilidad emocional se relacionan con un aumento de la ideación e intentos de suicidio en población comunitaria, y con una mayor ideación suicida en población clínica. Por su parte, el SCS no solo se asocia con la ideación e intentos suicidas, sino que también actúa como mediador entre la desregulación emocional y la conducta suicida en ambos tipos de población. Asimismo, las creencias de incontrolabilidad emocional y la percepción de ser una carga predicen la aparición del SCS a lo largo del tiempo en población comunitaria. Estos hallazgos sugieren que las diferencias individuales en la regulación emocional—particularmente en la rumiación y las creencias de incontrolabilidad—pueden mejorar la capacidad predictiva de los modelos de suicidio existentes. Además, resaltan la importancia de abordar estas variables, junto con la percepción de ser una carga, en las intervenciones de prevención del suicidio.Palabras clave:suicidio, regulación emocional, síndrome crisis suicida, creencias emocionales, rumiación
The Role of Context in School Disruption: A Training Proposal for Teachers
No full textEl presente Trabajo Fin de Grado nace de la unión de las prácticas externas I y II, un viaje hace el descubrimiento personal y profesional de las salidas formativas del Grado en Pedagogía. El inicio de esta aventura me deparó en el departamento de orientación educativa del I.E.S Ben Gabirol donde encontré situaciones de aprendizajes distintos que me hicieron cultivar nuevas perspectivas educativas y pedagógicas. En esta mi segunda odisea conseguí salir de mi zona de confort y encaminé un trayecto desconocido y novedoso para mí: la formación permanente del profesorado. Donde mejor que en la cuna de la educación continua del/la docente, el Centro del Profesorado de Málaga. Un lugar donde me ayudó a confeccionar un curso formativo dedicado al abordaje del conflicto y los comportamientos disruptivos en el aula, a través de una visión amplia y no selectiva. Con esto me refiero a que no existe un solo culpable tras un problema, sino una cadena de circunstancias y sucesos que permiten que se generen estas situaciones
On the participation of energy storage systems in reserve markets using Decision Focused Learning
Get PDFBattery Energy Storage Systems (BESSs) are particularly well-suited to deepen the decarbonisation of reserve markets, traditionally dominated by non-renewable generators. BESSs operators often rely on Predict-Then-Optimise (PTO) methods to participate in these markets, which focus on forecasting market conditions without directly considering the impact of subsequent decisions during training. Recently, learning models have evolved to incorporate decision outcomes during training, known as Decision Focused Learning (DFL) methodologies, which have the potential to increase market benefits. This paper introduces a DFL approach that integrates the decision-making process of BESSs when participating in reserve markets into the training of their predictive models. By expressing the optimisation problem as a primal–dual mapping using the Karush–Kuhn–Tucker (KKT) conditions, the proposed DFL method enables the regressor to learn from the BESS’s decisions, refining its predictions based on observed outcomes, improving decision accuracy and market performance. Results show that the proposed DFL approach outperforms traditional PTO methods, with up to a 9.5% increase in profits for a case study based on the Belgian secondary reserve market, highlighting its effectiveness in managing the complexities of dynamic market conditions.Funding for open access charge: Universidad de Málaga / CBU