bluuebunny/crossref_metadata_2025_split huggingface dataset:
Highly logical and non-emotional decisions in both risky and social contexts: understanding decision making in autism spectrum disorder through computational modeling
Authors: Molins Francisco , Ben-Hassen Jemni Nour , Garrote-Petisco Dolores , Serrano Miguel Ángel 
Published: March 2024

AbstractIn risky contexts, autism spectrum disorder (ASD) individuals exhibit more logical consistency and non-emotional decisions than do typical adults (TAs). This way of deciding could be also prevailing in social contexts, leading to maladaptive decisions. This evidence is scarce and inconsistent, and further research is needed. Recent developments in computational modeling allow analysis of decisional subcomponents that could provide valuable information to understand the decision-making and help address inconsistencies. Twenty-seven individuals with ASD and 25 TAs were submitted to a framing-task and the ultimatum game (UG). The Rescorla–Wagner computational model was used to analyze UG decisions. Results showed that in the UG, the ASD group exhibited a higher utilitarianism, characterized by lower aversion to unfairness and higher acceptance of offers. Moreover, this way of deciding was predicted by the higher economic rationality found in the framing task, where people with ASD did not manifest emotional biases such as framing effect. These results could suggest an atypical decision making, highly logical and non-emotional, as a robust feature of ASD.
Stand-level sampling designs for bark stripping caused by red deer (Cervus elaphus L.): simulation studies based on nine fully censused stands
Authors: Hahn Christoph , Vospernik Sonja 
Published: March 2024

AbstractPrecise assessment of bark stripping damage is of high economic importance, since bark stripping makes wood unusable for saw timber and it is important for compensation payments for game damage. Bark stripping is clustered and decreases with increasing tree diameter, so that common forest inventories, optimized for assessing timber production variables such as standing timber volume, do not provide adequately precise estimates of bark stripping damage. In this study we analysed different sampling designs (random sampling, systematic sampling), tree selection methods (fixed radius plot, angle count sampling) and number of plots and plot sizes (plot radius: 2–20 m; basal area factor: 1–6m2/ha) for bark stripping assessment. The analysis is based on simulation studies in 9 fully censused stands (9026 trees). Simulations were done for actually assessed damage and randomly distributed damage and each scenario was repeated 100 times with different random points or different random grid locations. Systematic sampling was considerably more precise than random sampling in both scenarios. Sampling intensities to attain a standard error of 10% ranged between 12 and 18% dependent on the plot size. For a given sampling intensity, precision increased with decreasing plot size or increasing basal area factor. This implies, however, a large number of plots to be measured, which is expensive, when travel costs are high. Differences between tree selection by fixed radius plots or angle count sampling were minor. For bark stripping damage, we recommend sampling with fixed radius plots with a radius of 4–6 m and the measurement of approximately 230 or 150 plots, respectively.
Loneliness, social support, and social networks: urban–rural variation and links to wellbeing in Scotland
Authors: Long Emily , Thomson Meigan , Milicev Jelena , Goodfellow Claire , Letina Srebrenka , Bradley Sara , McCann Mark 
Published: March 2024

Abstract
Aim
Social relationships are key public health priorities, with their relevance to wellbeing amplified in recent years. Relationships are embedded within the wider environment in which they occur; however, we lack understanding of whether, and how, places may affect social relationships. This study utilises an urban vs rural framework to examine variation in three specific aspects of relationships (loneliness, social support, and social networks), and their links to wellbeing.

Subjects and method
Cross-sectional social network data, collected in Scotland in 2021 (N = 191), was used. Bivariate comparisons tested for differences in loneliness, social support, and social networks between urban and rural participants. Multivariable regression tested for associations between each construct and wellbeing, and interaction effects assessed differences in associations between the urban and rural locations.

Results
Urban participants had higher levels of loneliness and poorer wellbeing, even though reported levels of social support didn’t differ. After adjusting for sociodemographic characteristics, loneliness, social support, and perceived emotional closeness of social networks were all associated with levels of wellbeing. There was no evidence that these associations differed between participants in urban and rural areas.

Conclusion
Overall, findings highlight key place-based differences that inform the design of efforts to promote social connectivity and enhance wellbeing. Given that urban residents had lower wellbeing and higher levels of loneliness, coupled with evidence demonstrating the importance of close, supportive social relationships, intervention efforts that promote social connectivity in urban environments are particularly needed.
Extended dissipative criteria for delayed semi-discretized competitive neural networks
Authors: Adhira B. , Nagamani G. 
Published: March 2024

AbstractThis brief investigates the extended dissipativity performance of semi-discretized competitive neural networks (CNNs) with time-varying delays. Inspired by the computational efficiency and feasibility of implementing the networks, we formulate a discrete counterpart to the continuous-time CNNs. By employing an appropriate Lyapunov–Krasovskii functional (LKF) and a relaxed summation inequality, sufficient conditions ensure the extended dissipative criteria of discretized CNNs are obtained in the linear matrix inequality framework. Finally, to refine our prediction, two numerical examples are provided to demonstrate the sustainability and merits of the theoretical results.
The Effect of Mindfulness-based Psychoeducation on Negative Automatic Thoughts and Medication Adherence in Individuals with Cannabis Use Disorder: a Randomized Controlled Trial
Authors: Budak Funda Kavak , Akbeniz Aysel , Erkan Fatma Melike , Gültekin Abdurrezzak , Cumurcu Hatice Birgül 
Published: March 2024

AbstractThe aim of this study is to evaluate the effect of mindfulness-based psychoeducation on negative automatic thoughts and medication adherence in individuals with cannabis use disorder. The randomized controlled study was conducted between September 2021 and December 2022 in the psychiatry clinic of a university hospital in Turkey. The sample size of the study consisted of 60 individuals (30 experimental groups, 30 control groups). Descriptive Characteristics Form, Negative Automatic Thoughts Questionnaire, and Morisky Medication Adherence Scale were used to collect data. In the study, while eight sessions of mindfulness-based psychoeducation were given to the experimental group, no intervention was applied to the control group. Experimental group posttest negative automatic thoughts and medication adherence scale total mean scores were found to be statistically significant (p < 0.05). It was determined that the posttest negative automatic thoughts scale total score mean in the experimental group decreased compared to the control group, and the medication adherence scale total score mean increased compared to the control group. It was determined that mindfulness-based psychoeducation given to individuals with cannabis use disorder was effective in negative automatic thoughts and adherence to treatment.
Connecting probability for random bounded-range one-dimensional network
Authors: Federico Lorenzo 
Published: March 2024

AbstractWe consider a class of bounded-range 1D network models on a cycle and prove that, unlike the corresponding infinite-volume models, which never contain infinite components, they actually exhibit a phase transition for connectivity. We further show that depending on the specific choice of the edge probabilities, the last obstruction to connectivity can either be the existence of isolated vertices or the split of the cycle into two spatially separated components.
Curriculare Analysen als Baustein der Assessmentkonstruktion
Authors: Fortunati Fabio , Winther Esther 
Published: March 2024

ZusammenfassungUm die oft kontrovers geführten Debatten von wirtschaftlichen Auswirkungen politischer Maßnahmen nachvollziehen zu können, bedarf es eines grundlegenden wirtschaftlichen Verständnisses. Die Entwicklung wirtschaftlicher Kompetenz stellt deshalb ein wichtiger Baustein für die Beteiligung des Individuums am gesellschaftlichen Leben dar. Die bisherige Beschreibung und Erfassung wirtschaftlicher Kompetenzen von Schüler*innen bezieht sich größtenteils auf die Sekundarstufe II und fokussiert auf fachwissenschaftliche Inhalte. Ziel dieses Beitrages ist es zu zeigen, wie die Assessmentkonstruktion in gering strukturierten Domänen durch Analysen des curricular intendierten fachlichen Inhalts und der kognitiven Prozesse bei der Aufgabenkonstruktion sowie bei der curricularen Validierung profitieren kann. Auf Grundlage der projektspezifischen Kompetenzdefinition und der Analyse der ökonomischen Domäne wurde ein deduktives Kategoriensystem entwickelt, um Lehrpläne der ökonomischen Bildung aus zehn verschiedenen Bundesländern komparativ zu analysieren. Dieses zeigt über die Curricula hinweg eine hohe Interrater-Reliabilität (0,60 ≤ κ ≤ 0,90). Die Ergebnisse der Analyse deuten sowohl auf Unterschiede mit Blick auf die zu vermittelnden fachlichen Inhalte als auch auf die intendierten kognitiven Prozesse des Lernens hin. Die Passung zwischen Testinstrument und Curricula ist über alle untersuchten Lehrpläne hinweg hoch (P = 0,820). Die Ergebnisse können wertvolle Hinweise für eine bessere Abgleichung und Entwicklung von Assessments bezogen auf curriculare Anforderungen geben.
A Comparative Study on Wear Resistance of Cold-Sprayed Aluminum/Quasicrystal Composite Coatings
Authors: Jafari Reza , Cizek Jan , Lukac Frantisek , Cvrcek Ladislav , Buril Matej , Walter Jan , Honkanen Mari , Vippola Minnamari , Koivuluoto Heli 
Published: March 2024

AbstractCold spray (CS) technology has proven a great potential in the production of composite coatings, enabling the production of materials with superior qualities such as enhanced tribological behavior. This study aims to investigate the tribological properties of CS Al-based composite coatings reinforced by quasicrystalline (QC) particles. Two different Al alloys were used as the matrix, AA 6061 and AA 2024, and mixed with Al-based QC particles (Al-Cr-Fe-Cu) at different Al/QC ratios. A room-temperature ball-on-disc test was then used to evaluate the wear resistance of the composite CS coatings in air and compared to those of the non-reinforced Al alloy CS coatings as well as a cast counterpart (AA 6061-T6). We have demonstrated that CS could be employed to produce thick and dense Al-QC composites that can retain up to about 50 wt.% QC reinforcement in the structure. The incorporation of the QC particles increased the wear resistance by a factor of 7.
Oxidative stress status and antioxidative responses in neonate versus adult Daphnia magna exposed to polystyrene leachate
Authors: Esterhuizen Maranda , Monticelli Michela , Lee Sang-Ah , Kim Youngsam , Pflugmacher Stephan , Kim Young Jun 
Published: March 2024

Abstract
Objective
Plastic pollution, particularly polystyrene, significantly threatens aquatic ecosystems worldwide. Furthermore, plastic leachates have been documented to be detrimental to some aquatic organisms; however, understanding the toxicity mechanism remains limited. This study aimed to investigate the ecotoxicological effects of polystyrene leachate on neonate and adult Daphnia magna, a keystone species in freshwater ecosystems.

Methods
The effects of the leachate were studied by employing the novel technique of separating daphnids from the polystyrene microplastic fragments via dialysis tubing, which was prepared 24 and 72 h before organism exposure. Acute toxicity was assessed as effects on organism mobility, oxidative stress (reactive oxygen species), antioxidative enzyme responses (superoxide dismutase and catalase), as well as the effects on the biotransformation enzyme glutathione S-transferase’s activity.

Results
Under the experimental conditions, the mobility and oxidative status of the daphnids were unaffected, irrespective of the organisms’ age or leaching time. In adults exposed for 24 h, the antioxidant defense enzyme activities were elevated, contributing to cellular homeostasis maintenance. However, the catalase activity was reduced for neonates and adults exposed to the prolonged pre-leached treatment, thus making them less capable of retaining homeostasis when exposed to toxicant mixtures.

Conclusion
This study highlights the vulnerability of D. magna to polystyrene leachate and underscores the need for continued research on the ecotoxicological effects of plastic pollution in aquatic ecosystems. Findings from this investigation contribute to understanding the ecological consequences of plastic pollution, which can inform mitigation strategies and policy decisions to preserve the health and integrity of freshwater ecosystems.
Obesity and Obesity-Related Thyroid Dysfunction: Any Potential Role for the Very Low-Calorie Ketogenic Diet (VLCKD)?
Authors: Chapela Sebastián Pablo , Simancas-Racines Alison , Ceriani Florencia , Martinuzzi Andrés Luciano Nicolas , Russo María Paula , Zambrano Ana Karina , Simancas-Racines Daniel , Verde Ludovica , Muscogiuri Giovanna , Katsanos Christos S. , Frias-Toral Evelyn , Barrea Luigi 
Published: March 2024

Abstract
Purpose of Review
This review aims to explore in-depth the different aspects of the association between very low-calorie ketogenic diet (VLCKD), obesity and obesity-related thyroid dysfunction.

Recent Findings
The VLCKD, proposed as a non-pharmacological strategy for the management of certain chronic diseases, is becoming increasingly popular worldwide. Initially used to treat epilepsy, it has been shown to be effective in controlling body weight gain and addressing various pathophysiological conditions. Research has shown that a low-calorie, high-fat diet can affect thyroid hormone levels. Weight loss can also influence thyroid hormone levels. Studies have suggested that long-term use of VLCKD for refractory epilepsy may be related to the development of hypothyroidism, with an effect seen in various populations. In particular, women with obesity following VLCKD tend to have reduced T3 levels.

Summary
We propose further research to unravel the underlying mechanisms linking VLCKD to obesity and obesity-related thyroid dysfunction.
Autism Spectrum Disorder Symptoms in Individuals with a Primary Diagnosis of Attention-Deficit/Hyperactivity Disorder: A Systematic Review
Authors: Zhong Qing , Porter Melanie 
Published: March 2024

Abstract
Background and Aims
Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) represent two of the most prevalent neurodevelopmental conditions, with prevalence rates of 5–7% and 0.6–1%, respectively. Despite their conceptually distinct phenotypes, ADHD and ASD are highly comorbid. This review is aimed at examining and synthesising the prevalence and profile of ASD-like symptoms in individuals with a primary diagnosis of ADHD across the lifespan in accordance with the Diagnostic and Statistical Manual, Fifth Edition (DSM-5; APA, 2013) diagnostic criteria.

Methods
This systematic review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, as discussed by (Moher et al. in PLOS Medicine, 6(7), e1000097, 2009; Page et al. in Systematic Reviews, 10(1), 89, 2021). Studies were identified through a systematic literature search of the databases: PsycINFO, Medline, CINAHL, Psychology and Behavioural Sciences Collection, and Scopus (N = 12,633). Articles were critically appraised according to established quality methodology criteria, and findings were summarised using narrative synthesis.

Results and Conclusion
A total of nine studies (ADHD individuals n = 548) were identified and included in the review. Of these studies, three were classified as having ‘good’ methodological quality, five as ‘fair’, and one as ‘poor’. These studies varied considerably in their methodological approach and assessment procedures. Overall, higher levels of ASD symptoms were found in individuals with ADHD compared to the general population. There was considerable variation in the reported prevalence of clinically significant ASD symptoms in individuals with ADHD, ranging from 15 to 64.3%. Clinical implications and considerations for further research are discussed.
Meta learning-based few-shot intrusion detection for 5G-enabled industrial internet
Authors: Yan Yu , Yang Yu , Shen Fang , Gao Minna , Gu Yuheng 
Published: March 2024

AbstractWith the formation and popularization of the 5G-enabled industrial internet, cybersecurity risks are increasing, and the limited number of attack samples, such as zero-day, leaves a short response time for security protectors, making it substantially more difficult to protect industrial control systems from new types of malicious attacks. Traditional supervised intrusion detection models rely on a large number of samples for training and their performance needs to be improved. Therefore, there is an urgent need for few-shot intrusion detection. Aiming at the above problems, this paper proposes a detection model based on a meta-learning framework, which aims to effectively improve the accuracy and real-time performance of intrusion detection, and designs a meta-learning intrusion detection model containing a sample generation module, a feature mapping module and a feature metric module. Among them, the sample generation module introduces the residual block into the Natural GAN and proposes a new method to generate high-quality antagonistic samples—Res-Natural GAN, which is used to enhance the antagonism of the generated samples and the feature mining degree, to improve the accuracy of malicious traffic detection; the feature mapping module proposes a new attention mechanism, the multi-head fast attention mechanism, which is applied to the encoder structure of the transformer and combined with a parameter optimization algorithm based on particle swarm mutation to shorten the mapping time and improve the real-time performance of the model while mapping the features effectively; the feature metric module proposes a prototype structure based on a prototype storage update algorithm and combines it with a prototype network to achieve correct classification by measuring the Euclidean distance between the detected samples and the class of prototypes, and to shorten the inference time while ensuring the detection accuracy; finally, the three modules are combined to form a real-time meta-learning intrusion detection model. To evaluate the proposed model, five different types of experiments are conducted on multiple public datasets. The experimental results show that the model has higher detection accuracy than the traditional model for both few-shot and zero-shot malicious attacks, and is not only applicable to 5G-enabled industrial internet, but also generalized to different network environments and attack types.
Forecasting implied volatilities of currency options with machine learning techniques and econometrics models
Authors: Olsen Asbjørn , Djupskås Gard , de Lange Petter Eilif , Risstad Morten 
Published: March 2024

AbstractDeveloping an effective modeling framework to minimize foreign exchange (FX) risk is of vital importance for hedgers and traders in FX markets. In this study, we compare the ability of long short-term memory (LSTM) models to that of random forest and several time series models for forecasting EURUSD implied volatility across the volatility surface. As our literature study argues, there are only a few published papers on this subject. We find that the LSTM model is the best model for shorter option maturities, while the AR-GARCH model is superior when the maturities increase. We observe that the LSTM model is able to capture immense and immediate changes in implied volatility, which is important for hedging against significant shifts in FX rates.
Developing a Framework for Self-regulatory Governance in Healthcare AI Research: Insights from South Korea
Authors: Kim Junhewk , Kim So Yoon , Kim Eun-Ae , Sim Jin-Ah , Lee Yuri , Kim Hannah 
Published: March 2024

AbstractThis paper elucidates and rationalizes the ethical governance system for healthcare AI research, as outlined in the ‘Research Ethics Guidelines for AI Researchers in Healthcare’ published by the South Korean government in August 2023. In developing the guidelines, a four-phase clinical trial process was expanded to six stages for healthcare AI research: preliminary ethics review (stage 1); creating datasets (stage 2); model development (stage 3); training, validation, and evaluation (stage 4); application (stage 5); and post-deployment monitoring (stage 6). Researchers identified similarities between clinical trials and healthcare AI research, particularly in research subjects, management and regulations, and application of research results. In the step-by-step articulation of ethical requirements, this similarity benefits from a reliable and flexible use of existing research ethics governance resources, research management, and regulatory functions. In contrast to clinical trials, this procedural approach to healthcare AI research governance effectively highlights the distinct characteristics of healthcare AI research in research and development process, evaluation of results, and modifiability of findings. The model exhibits limitations, primarily in its reliance on self-regulation and lack of clear delineation of responsibilities. While formulated through multidisciplinary deliberations, its application in the research field remains untested. To overcome the limitations, the researchers’ ongoing efforts for educating AI researchers and public and the revision of the guidelines are expected to contribute to establish an ethical research governance framework for healthcare AI research in the South Korean context in the future.