Background: The phenomenon of "empty nest youth" is becoming increasingly ubiquitous, capturing the attention of society at large. However, few studies have been conducted in recent years on this group, especially focusing on their family and mental health. As such, this study investigates the correlation between family health and well-being among "empty nest youth," as well as the function of social support and loneliness in this relationship. Methods: A cross-sectional survey was conducted from June to August 2022 across 32 provinces, municipalities, and autonomous regions in China, utilizing a multi-stage sampling technique. And we screened individuals who were unmarried, living alone, and between 22-44 years old, resulting in a valid sample size of 908 cases; multiple regression analysis, mediation effect testing, and moderation effect testing are used to examine research hypotheses. Results: The regression analysis results show that family health not only has a direct impact on well-being (β = 0.36, p < 0.001) but also indirectly affects well-being through social support [β = 0.23, 95% CI: 0.19 0.28]. Additionally, the loneliness moderates the predictive impact of not only family health on social support (β = -0.13, p < 0.001) but also social support on well-being (β = -0.06, p < 0.001). Conclusions: These findings underscore the significance of directing policymakers and healthcare professionals towards the "empty nest youth's" familial and social support systems. It underscores the need for the development of policies aimed at addressing their emotional and material requirements by leveraging these familial and social networks. This approach ultimately contributes to the enhancement of their overall psychological well-being, promoting a more coherent and logical pathway for intervention and support.

With the rapid growth of online communication platforms, the identification and management of toxic comments have become crucial in maintaining a healthy online environment. Various machine learning approaches have been employed to tackle this problem, ranging from traditional models to more recent attention-based transformer networks. This paper aims to compare the performance of attention-based transformer networks with several traditional machine learning methods for toxic comments classification. We present an in-depth analysis and evaluation of these methods using a common benchmark dataset. The experimental results demonstrate the strengths and limitations of each approach, shedding light on the suitability and efficacy of attention-based transformers in this domain.

The aim of this article is to examine the impacts of Coronavirus Disease -19 (Covid-19) vaccines on economic condition and sustainable development goals. In other words, we are going to study the economic condition during Covid19. We have studied the economic costs of pandemic, benefits in terms of gross domestic product (GDP), public finances and employment, investment on vaccines around the world, progress and totally the economic impacts of vaccines and the impacts of emerging markets (EM) on achieving sustainable development goals (SDGs), including no poverty, good health and well-being, zero hunger, reduced inequality etc. The importance of emerging economies in reducing the harmful effects of the Corona has also been noted. We have tried to do experimental results and forecast daily new death cases from Feb-2020 to Aug-2021 in Iran using Artificial Neural Network (ANN) and Beetle Antennae Search (BAS) algorithm as a case study with econometric models and regression analysis. The findings show that Covid19 has had devastating economic and health effects on the world, and the vaccine can be very helpful in eliminating these effects specially in long-term. We observed that there is inequality in the distribution of Corona vaccines in rich countries compared to poor which EM can decrease the gap between them. The results show that both models (i.e., Artificial intelligence (AI) and econometric models) almost have the same results but AI optimization models can robust the model and prediction. The main contribution of this article is that we have surveyed the impacts of vaccination from socio-economic viewpoint not just report some facts and truth. We have surveyed the impacts of vaccines on sustainable development goals and the role of EM in achieving SDGs. In addition to using the theoretical framework, we have also used quantitative and empirical results that have rarely been seen in other articles.

Nowadays, graphs including bigraphs are mostly used in various real-world applications such as search engines and social networks. The bigraph or bipartite graph is a graph whose vertex set is split into two disjoint vertex sets such that there is no edge between the same vertex set. The bipartite graphs are colored using only two colors. This article checks if a given graph is bipartite or not and finds the color assignments of the bipartite graph using Java implementation.

The reduction of the native forests coverage in mainland Portugal increased in the past centuries, leading to a marked decrease in biodiversity in general, especially on typical species of mature forest environments. However, urban biodiversity seems to resist more effectively than rural to disturbances due to the lower incidence of fires, as well as to agriculture expansion. Thus, in this work, we analyzed the dynamics of the natural vegetation potential in each biogeographic sector, and selected, based on the evolutionary stages of the vegetation, a set of priority taxa for conservation. The criteria used are intended to highlight plants with ornamental value, but at the same time, some of them have high patrimonial value, belonging to the Red List of Vascular Flora of Mainland Portugal or protected by Annexes II, IV and V of the Sectorial Plan of the Natura 2000 Network at the European level. Our analysis resulted in the identification of 62 plants that can be increased in public spaces in order to improve their conservation status. For each biogeographic sector, the plants best adapted to the local edaphoclimatic conditions are presented. Forest habitats can now, through micro-reserves in urban areas, ensure their long-term conservation and greater awareness among the population. An integrated planning, where the socio-ecological strategy is designed for the long term, will benefit the quality of life of citizens in an urban environment. Furthermore, the creation of micro-reserves in urban parks (gardens) can prevent the extinction of many botanical values in the landscapes of the western Mediterranean Basin.

Three novel coordination polymers {[Cu₂(bitbu-OMe)₄(SO₄)₂]·6MeOH}_n (1), {[Co₂(bitbu-OMe)₄(NCS)₄]_0.5·2DMF}_n (2), {[Co(bitbu-OMe)₂(NCS)₂]·2MeOH}_n (3) (bitbu-OMe = 1,1’-[(5-tert-butyl-2-methoxybenzene-1,3-diyl)dimethanediyl]bis(1H-imidazole)) are synthesized through a slow evaporation method using solvothermal technique of CuSO₄·5H₂O or Co(SCN)₂ with bitbu-OMe. X-ray diffraction analysis results reveal that 1, 2, and 3 have similar two-dimensional layer networks. The study of the effect of the methoxy group in bitbu-OMe towards the stability of ligand conformation in obtained coordination polymers becomes necessary to be conducted in the future to unveil the reason for conformation similarity of ligand in coordination polymers.

Background: Resting-state functional MRI (rsfMRI) provides insights into large-scale brain network organization associated with cognitive control, emotion regulation, and attentional processes. The ventral attention network (VAN) is a key salience-driven network that supports attentional re-orienting to behaviorally relevant stimuli. However, little is known about how VAN resting state functional connectivity varies by demographic, socioeconomic, psychosocial, and behavioral factors during early adolescence. Objective: To examine associations between VAN rsfMRI connectivity and multiple demographic, socioeconomic, psychosocial, and behavioral characteristics. Methods: Data came from the baseline and early follow-up waves of the Adolescent Brain Cognitive Development (ABCD) Study. The analytic sample included youth with high-quality baseline rsfMRI data and complete socioeconomic and psychosocial measures. The primary outcome was mean resting-state functional connectivity within the VAN across subcortical and cortical regions of interest (ROIs). Bivariate correlations were computed between VAN connectivity and demographic (age, sex, puberty, race/ethnicity), socioeconomic (income, parental education, marital status, neighborhood income), psychosocial (trauma, discrimination, financial difficulty), trait (impulsivity), and behavioral variables (body mass index, depression, suicide, prodromal symptoms, and substance use). Unadjusted bivariate correlations and adjusted logistic regressions were used for data analysis. Results: VAN connectivity showed small but significant correlations with multiple contextual factors. Higher household income, parental education, and neighborhood affluence were associated with greater connectivity, whereas Black race and Hispanic ethnicity were related to lower connectivity. Youth reporting higher discrimination and financial difficulty exhibited weaker VAN connectivity. Greater VAN connectivity was negatively associated with impulsive reward-driven trait (drive), prodromal symptoms, BMI, and marijuana and alcohol use. Associations between VAN connectivity and suicide, depression, marijuana use, and alcohol use remained significant in age and sex adjusted models. Conclusions: VAN connectivity reflects subtle neural correlates of socioeconomic and psychosocial context in early adolescence. Our results underscore the importance of integrating structural and contextual factors in interpreting brain-behavior associations across diverse populations. These findings are suggestive of stable socioeconomic and psychosocial correlates of network efficiency.

Early-onset neurodegenerative diseases (EO-NDs), such as early-onset Alzheimer’s disease (EOAD), Parkinson’s disease (EOPD), and familial amyotrophic lateral sclerosis (fALS), often stem from monogenic causes and manifest before typical age thresholds. These disorders frequently feature disrupted mitochondrial function and heightened oxidative stress, which together accelerate neuronal damage and degeneration. In this work, the author performs a comprehensive analysis of the literature and data related to mitochondrial dysfunction and redox imbalance in EO-NDs. Bibliometric trends were assessed using R-based tools on PubMed datasets, highlighting keyword networks and publication surges in recent years. Publicly available RNA-seq datasets from GEO and SRA were examined, with example DESeq2 analysis illustrating altered mitochondrial gene expression in EO-ND patient-derived samples. Network modeling of redox pathways using Python’s networkx demonstrates how oxidative stress can propagate through metabolic networks. Together, these computational approaches reinforce that mitochondrial DNA mutations, impaired electron transport chain (ETC) function, and reactive oxygen species (ROS) accumulation play central roles in EO-ND pathogenesis. The discussion further evaluates why antioxidant clinical trials have largely failed and how emerging therapies such as gene replacement, antisense oligonucleotides, and mitochondrial biogenesis modulators may provide more effective interventions.

The gut-brain axis (GBA) has emerged as a central focus in the study of neurodevelopmental disorders, particularly autism spectrum disorder (ASD). Research suggests that microbial composition and its metabolic byproducts influence neural development, synaptic plasticity, and behavior [1,2,3]. A structured bibliometric analysis of Scopus and Web of Science records was performed using Bibliometrix and VOSviewer to trace trends and thematic evolution of GBA–ASD literature [7,8]. In parallel, a data-driven pathway modeling approach maps microbial metabolites (e.g., short-chain fatty acids, tryptophan catabolites) to host signaling pathways including vagal stimulation, immune cytokine modulation, and blood–brain barrier (BBB) permeability [4,5]. Simulations implemented in Python’s NetworkX illustrate how perturbations in metabolite flux may influence CNS outcomes. The findings reveal growing emphasis on butyrate, serotonin, microglial priming, and maternal immune activation in ASD-related GBA studies, and highlight the need for rigorous empirical validation of computational predictions [9,10,11].