Analysis of Digital Self-Presentation Practices and Profiles of Spanish Adolescents on Instagram and TikTok

This study analyses self-presentation practices and profiles among Spanish teenagers on Instagram and TikTok. Both of these online spaces prioritise and promote visual publications, are structured to allow feedback on self-presentation, and offer the user filters both to control self-image and to target specific audiences. Three research questions guided the methodological process for the twofold analysis of self-presentation practices on social networks: an exploratory factor analysis to identify latent factors among these practices; and a descriptive analysis of the profiles identified by gender and age. Results indicate that adolescents’ self-presentation practices were related to three different factors: social validation; authenticity; and image control. One of the most outstanding results is that self-presentation practices could be less guided by social feedback, since the number of followers or likes was irrelevant for most adolescents, and that adolescents increasingly tend to be guided by innovative predispositions of truthfulness. In turn, conclusions suggest that teens need to be equipped with suitable self-representation practices for safe and sustainable identity narratives on social networks, since the global COVID-19 pandemic has exponentially increased both the usage and the time spent on social networking sites, enlarging the availability of spaces for adolescents to express themselves and build their identities through different self-representation practices.

Case-control study of adverse childhood experiences and multiple sclerosis risk and clinical outcomes

Background Adverse childhood experiences (ACEs) are linked to numerous health conditions but understudied in multiple sclerosis (MS). This study’s objective was to test for the association between ACEs and MS risk and several clinical outcomes. Methods We used a sample of adult, non-Hispanic MS cases (n = 1422) and controls (n = 1185) from Northern California. Eighteen ACEs were assessed including parent divorce, parent death, and abuse. Outcomes included MS risk, age of MS onset, Multiple Sclerosis Severity Scale score, and use of a walking aid. Logistic and linear regression estimated odds ratios (ORs) (and beta coefficients) and 95% confidence intervals (CIs) for ACEs operationalized as any/none, counts, individual events, and latent factors/patterns. Results Overall, more MS cases experienced ≥1 ACE compared to controls (54.5% and 53.8%, respectively). After adjusting for sex, birthyear, and race, this small difference was attenuated (OR = 1.01, 95% CI: 0.87, 1.18). There were no trends of increasing or decreasing odds of MS across ACE count categories. Consistent associations between individual ACEs between ages 0–10 and 11–20 years and MS risk were not detected. Factor analysis identified five latent ACE factors, but their associations with MS risk were approximately null. Age of MS onset and other clinical outcomes were not associated with ACEs after multiple testing correction. Conclusion Despite rich data and multiple approaches to operationalizing ACEs, no consistent and statistically significant effects were observed between ACEs with MS. This highlights the challenges of studying sensitive, retrospective events among adults that occurred decades before data collection.

A Rating Prediction Recommendation Model Combined with the Optimizing Allocation for Information Granularity of Attributes

In recent years, graph neural networks (GNNS) have been demonstrated to be a powerful way to learn graph data. The existing recommender systems based on the implicit factor models mainly use the interactive information between users and items for training and learning. A user–item graph, a user–attribute graph, and an item–attribute graph are constructed according to the interactions between users and items. The latent factors of users and items can be learned in these graph structure data. There are many methods for learning the latent factors of users and items. Still, they do not fully consider the influence of node attribute information on the representation of the latent factors of users and items. We propose a rating prediction recommendation model, short for LNNSR, utilizing the level of information granularity allocated on each attribute by developing a granular neural network. The different granularity distribution proportion weights of each attribute can be learned in the granular neural network. The learned granularity allocation proportion weights are integrated into the latent factor representation of users and items. Thus, we can capture user-embedding representations and item-embedding representations more accurately, and it can also provide a reasonable explanation for the recommendation results. Finally, we concatenate the user latent factor-embedding and the item latent factor-embedding and then feed it into a multi-layer perceptron for rating prediction. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework.

The Importance of Service Attributes between Low-cost and Full-service Carrier Customers: A case of Airline Type Hybridisation

This study sought to assess if the importance attached by customers to the airline service attributes differed across low-cost and full-service airline models. A Mann-Whitney U Test was used to assess the difference between the two models. However, before subjecting the data to differential tests, an exploratory factor analysis (maximum likelihood) was performed on the fifty-five items of service attributes, reducing them into forty-two items retained into ten latent factors (airline service attributes). The results of the revealed a significant difference in the importance attached to staff competence, courtesy and responsiveness only. Such findings suggest that the positioning of airlines into binary (FSC - LCC) models could be a waste of effort and resources since airlines seem to be converging.

Body Mass Index Moderates Brain Dynamics and Executive Function: A Structural Equation Modeling Approach

Obesity is associated with negative physical and mental health outcomes. Being overweight/obese is also associated with executive functioning impairments and structural changes in the brain. However, the impact of body mass index (BMI) on the relationship between brain dynamics and executive function (EF) is unknown. The goal of the study was to assess the modulatory effects of BMI on brain dynamics and EF. A large sample of publicly available neuroimaging and neuropsychological assessment data collected from 253 adults (18–45 years; mean BMI 26.95 kg/m2 ± 5.90 SD) from the Nathan Kline Institute (NKI) were included (http://fcon_1000.projects.nitrc.org/indi/enhanced/). Participants underwent resting-state functional MRI and completed the Delis-Kaplan Executive Function System (D-KEFS) test battery (1). Time series were extracted from 400 brain nodes and used in a co-activation pattern (CAP) analysis. Dynamic CAP metrics including dwell time (DT), frequency of occurrence, and transitions were computed. Multiple measurement models were compared based on model fit with indicators from the D-KEFS assigned a priori (shifting, inhibition, and fluency). Multiple structural equation models were computed with interactions between BMI and the dynamic CAP metrics predicting the three latent factors of shifting, inhibition, and fluency while controlling for age, sex, and head motion. Models were assessed for the main effects of BMI and CAP metrics predicting the latent factors. A three-factor model (shifting, inhibition, and fluency) resulted in the best model fit. Significant interactions were present between BMI and CAP 2 (lateral frontoparietal (L-FPN), medial frontoparietal (M-FPN), and limbic nodes) and CAP 5 (dorsal frontoparietal (D-FPN), midcingulo-insular (M-CIN), somatosensory motor, and visual network nodes) DTs associated with shifting. A higher BMI was associated with a positive relationship between CAP DTs and shifting. Conversely, in average and low BMI participants, a negative relationship was seen between CAP DTs and shifting. Our findings indicate that BMI moderates the relationship between brain dynamics of networks important for cognitive control and shifting, an index of cognitive flexibility. Furthermore, higher BMI is linked with altered brain dynamic patterns associated with shifting.

A fully supervised universal adversarial perturbations and the progressive optimization

Universal Adversarial Perturbations(UAPs), which are image-agnostic adversarial perturbations, have been demonstrated to successfully deceive computer vision models. Proposed UAPs in the case of data-dependent, use the internal layers’ activation or the output layer’s decision values as supervision. In this paper, we use both of them to drive the supervised learning of UAP, termed as fully supervised UAP(FS-UAP), and design a progressive optimization strategy to solve the FS-UAP. Specifically, we define an internal layers supervised objective relying on multiple major internal layers’ activation to estimate the deviations of adversarial examples from legitimate examples. We also define an output layer supervised objective relying on the logits of output layer to evaluate attacking degrees. In addition, we use the UAP found by previous stage as the initial solution of the next stage so as to progressively optimize the UAP stage-wise. We use seven networks and ImageNet dataset to evaluate the proposed FS-UAP, and provide an in-depth analysis for the latent factors affecting the performance of universal attacks. The experimental results show that our FS-UAP (i) has powerful capability of cheating CNNs (ii) has superior transfer-ability across models and weak data-dependent (iii) is appropriate for both untarget and target attacks.

Factors affecting hospital costs and revenue: integrating expert opinions and literature review

PurposeThere is not enough comprehensive evidence on factors affecting hospital costs and revenue (HCR). The main objective of the current study is to identify and classify factors affecting HCR integrating experts' opinions and literature review.Design/methodology/approachFirst, a restricted literature review is conducted to identify the factors affecting HCR. In the second step, the targeted semi-structured interviews are conducted with 15 experts to identify, validate and classify the latent factors.FindingsIn addition to the factors identified through the literature review, 22 new important factors were added by the experts as the determinants of HCR, which were not pointed out in previous studies. The final model presented for the factors affecting HCR contains seven main groups, 22 subgroups and 70 variables.Originality/valueFactors affecting HCR will provide valuable contributions for hospital budgeting, and financial and strategic planning, and they will offer an effective horizon for future research on cost-cutting strategies.

Factors affecting the intention of sustainable agriculture practices among pepper farmers in Sarawak, Malaysia

The demand for safe, hygienic, organic, and high-quality food products nowadays puts pressure on farmers to produce and practice sustainability. Sustainable agriculture practices (SAPs) are crucial to be implemented on every farm that produced foods to enable the supply of hygienic, safe food products and as a solution of pesticide-residue problems towards a healthier lifestyle. However, the farmers' main challenges towards sustainability and hindering their penetration to the global market are the difficulties in complying with the international standard of quality and certification compliance, such as MyGAP or Malaysian Good Agricultural Practices in cultivating, harvesting, and processing. MyGAP compliance showed that farmers are moving towards sustainable agriculture. This paper was aimed to assess the perception and the factors that influence the Intention to implement SAPs in pepper cultivation among pepper farmers in Sarawak. Descriptive analysis and factor analysis were used to accomplish the objectives of this study. Data collection was through interviews using a structured questionnaire administered on registered farmers under the Malaysian Pepper Board (MPB), planted at least 1 hectare or 2000 pepper trees in Bau and the District of Serian, Sarawak. Systematic stratified random sampling method was used based on the two different districts selected as stratification. The Theory of Planned Behaviour (TPB) was used as the conceptual framework to explain the farmers' behaviour towards SAPs. The results discovered four latent factors influencing Intention to practice SAPs: attitude, subjective norms, perceived behavioural Intention, and Intention, with the value of percentages of variance, explained 13.554%, 27.912%, 12.506%, 8.771%, and 7.703%, respectively. Subjective norms showed a high value of alpha at 0.935, followed by attitude (0.817) and Intention towards Sustainable Agriculture Practices (0.804). The findings provided the pepper farmers with invaluable insight on the advantages of adopting sustainable agriculture practices to expand their business locally and intentionally.