Articles

<b>Background</b><b>:</b><b> </b>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. <b>Objective</b><b>:</b><b> </b>To examine associations between VAN rsfMRI connectivity and multiple demographic, socioeconomic, psychosocial, and behavioral characteristics. <b>Methods</b><b>:</b><b> </b>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. <b>Results</b><b>:</b><b> </b>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. <b>C</b><b>onclusions</b><b>:</b><b> </b>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.

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].

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.

This research is a collaboration involving a university team, a partnering corporation, and a hemodialysis clinic, which is a cross-disciplinary research initiative in the field of Artificial Intelligence of Things (AIoT) within the medical informatics domain. The research has two objectives: (1) The development of an <i>Internet of Things (IoT)</i><i>-based</i><i> Information System</i> customized for the hemodialysis machines at the clinic, including transmission bridges, clinical personnel dedicated web/app, and a backend server. The system has been deployed at the clinic and is now officially operational; (2) The research also utilized de-identified, anonymous data (collected by the officially operational system) to train, evaluate, and compare <i>Deep Learning-based Intradialytic Blood Pressure</i><i> (BP)</i><i>/Pulse </i><i>Rate (PR) </i><i>Predictive Models</i>, with subsequent suggestions provided. Both objectives were executed under the supervision of the Institutional Review Board (IRB) at Mackay Memorial Hospital in Taiwan. The system completed for objective one has introduced three significant services to the clinic, including automated hemodialysis data collection, digitized data storage, and an information-rich human-machine interface as well as graphical data displays, which replaces traditional paper-based clinical administrative operations, thereby enhancing healthcare efficiency. The graphical data presented through web and app interfaces aids in real-time, intuitive comprehension of the patients’ conditions during hemodialysis. Moreover, the data stored in the backend database is available for physicians to conduct relevant analyses, unearth insights into medical practices, and provide precise medical care for individual patients. The training and evaluation of the predictive models for objective two, along with related comparisons, analyses, and recommendations, suggest that in situations with limited computational resources and data, an Artificial Neural Network (ANN) model with six hidden layers, SELU activation function, and a focus on artery-related features can be employed for hourly intradialytic BP/PR prediction tasks. It is believed that this contributes to the collaborating clinic and relevant research communities.

</b>Background: </b>The Cingulo-Opercular Network (CON) is a crucial executive control network involved in regulating actions and facilitating higher-order cognitive processes. Resting-state functional connectivity between the CON and the Default Mode Network (DMN) plays a vital role in cognitive regulation, enabling the transition between internally focused and externally directed tasks. This study investigates whether resting-state functional connectivity between the CON and DMN mediates the effects of social determinants, such as educational opportunities and family structure, on cognitive outcomes in youth. <b>Aims: </b>This study aims to explore how CON-DMN connectivity influences the relationship between social gradients and cognition in youth. Specifically, it examines whether resting-state functional connectivity between these networks mediates the effects of educational opportunities and family structure on cognitive outcomes and seeks to uncover the neural mechanisms underlying these social gradients. <b>Methods: </b>Data were derived from the Adolescent Brain Cognitive Development (ABCD) study, a large longitudinal dataset of over 11,000 children aged 9–10 years. Cognitive outcomes were assessed using standardized NIH toolbox measures: Total Composite, Fluid Reasoning, Picture Vocabulary, Pattern Recognition, and Card Sorting. Social determinants were operationalized using indicators such as parental education, family composition, and neighborhood educational opportunities (COI). Resting-state functional connectivity (rsFC) between the CON and DMN was measured using functional magnetic resonance imaging (fMRI). Structural equation modeling (SEM) was employed to test whether CON-DMN rsFC mediated the relationship between social determinants and cognitive outcomes, adjusting for potential confounders such as age, sex, and race/ethnicity. <b>Results: </b>Stable family structure and greater educational opportunities were significantly associated with improved cognitive performance. These relationships were mediated by reduced functional connectivity between the CON and DMN. <b>Conclusion: </b>Reduced functional connectivity between the CON and DMN serves as a neural mechanism linking social gradients, such as educational opportunities and family structure, to better cognitive outcomes in youth.

<b>Background: </b>While understanding how corticostriatal connectivity is associated with socioeconomic status (SES), trauma exposure, cognitive function, reward salience, impulsivity, and future substance use is essential to identifying neurobiological pathways that contribute to health disparities and behavioral outcomes, very few studies have tested the role of left caudate resting-state functional connectivity (rsFC) with the cingulo-opercular network as a proxy of corticostriatal connectivity in social, cognitive, and behavioral processes. <b>Objective: </b>This study investigates the associations between left caudate-cingulo-opercular connectivity and multiple biopsychosocial domains, including low SES, high trauma exposure (financial and life events), cognitive function, reward salience, impulsivity, depression, and future substance use (tobacco and marijuana use). <b>Methods: </b>Resting-state functional magnetic resonance imaging (rs-fMRI) data were analyzed to assess connectivity between the left caudate and the cingulo-opercular network. Data on socioeconomic status, trauma exposure, cognitive performance, and mental health were collected from participants. Future substance use behaviors were evaluated through longitudinal follow-ups. Correlation and regression analyses were conducted to examine relationships between corticostriatal connectivity and the targeted domains. <b>Results: </b>Corticostriatal hypoconnectivity was associated with lower SES, higher trauma exposure, poorer cognitive function, heightened reward salience, higher impulsivity, and history of depression. Additionally, corticostriatal hypoconnectivity at baseline predicted future tobacco and marijuana use during follow-up years. <b>Conclusion: </b>Corticostriatal hypoconnectivity, particularly the rsFC between the left caudate and the cingulo-opercular network, may represent a potential mechanism linking a wide range of social, emotional, and behavioral problems in youth. These findings suggest that corticostriatal hypoconnectivity could serve as a neurobiological marker for identifying individuals at risk for depression, low cognitive function, high reward salience, impulsivity, and substance use, emphasizing the interplay between socioeconomic and neurocognitive factors in shaping behavioral health trajectories.

Over the past few decades, cardiovascular disease and related complications have surpassed all others as the important causes of death on a universal scale. At the moment, they are the important cause of mortality universal, including in India. It is important to know how to find cardiovascular problems early so that patients get better care and prices go down. This project utilizes the UCI Heart Disease Dataset to develop ML and DL models capable of detecting cardiac diseases. Heart illness was categorized using Convolutional Neural Network (CNN) models, which are able to detect intricate patterns in supplied data. A confusion matrix rating, an F1-score, a ROC curve, accuracy, precision, and recall were some of the measures used to grade the model. It did much better than the Neural Network, Deep Neural Network (DNN), and Gradient Boosted Trees (GBT) models, with 91.71% accuracy, 88.88% precision, 82.75% memory, and 85.70% F1-score. Comparative study showed that CNN was the most accurate model. Other models had different balances between accuracy and recall. The experiment results show that the optional CNN model is a decent way to identify cardiovascular disease. This means that it could be used in healthcare systems to find diseases earlier and treat patients better.

High-Performance Computing (HPC) is indispensable for large-scale scientific simulations, but achieving optimal performance on modern supercomputers is increasingly challenging. As HPC systems scale toward exascale, they face escalating complexity in hardware, software, and workloads. Traditional optimization methods (manual tuning and heuristic algorithms) struggle to cope with dynamic workloads and intricate system behaviors. Artificial Intelligence (AI) techniques offer a promising approach to address these challenges. This article provides an overview of AI-powered optimization in HPC, focusing on how machine learning and related AI methods enhance performance, efficiency, and scalability of scientific simulations. We survey key AI techniques applied to HPC optimization including machine learning for performance modeling, deep reinforcement learning for resource management, and AI-driven surrogate models for accelerating simulations and illustrate their impact through case studies in domains such as job scheduling and fluid dynamics. We discuss the practical applications of these techniques, highlighting reported performance gains (e.g., substantial reductions in simulation run time and improved resource utilization). We also examine the challenges in integrating AI with HPC (such as training overhead, data movement, and reliability concerns) and outline future directions for research. The convergence of AI and HPC is poised to produce “smart” simulation workflows that intelligently adapt and optimize in real time, pushing the frontiers of scientific computing.

Business merchants and investors in Nigeria are interested in the foreign exchange volatility forecasting accuracy performance because they need information on how volatile the exchange rate will be in the future. In the paper, we compared Exponential Generalized Autoregressive Conditional Heteroskedasticity with order p=1 and q= 1, (EGARCH (1,1)) and Recurrent Neural Network (RNN) based on long short term memory (LSTM) model with the combinations of p = 10 and q = 1 layers to model the volatility of Nigerian exchange rates. Our goal is to determine the preferred model for predicting Nigeria’s Naira exchange rate volatility with Euro, Pounds and US Dollars. The dataset of monthly exchange rates of the Nigerian Naira to US dollar, Euro and Pound Sterling for the period December 2001 – August 2023 was extracted from the Central Bank of Nigeria Statistical Bulletin. The model efficiency and performance was measured with the Mean Squared Error (MSE) criteria. The results indicated that the Nigeria exchange rate volatility is asymmetric, and leverage effects are evident in the results of the EGARCH (1, 1) model. It was observed also that there is a steady increase in the Nigeria Naira exchange rate with the euro, pounds sterling and US dollar from 2016 to its highest peak in 2023. Result of the comparative analysis indicated that, EGARCH (1,1) performed better than the LSTM model because it provided a smaller MSE values of 224.7, 231.3 and 138.5 for euros, pounds sterling and US Dollars respectively.

The stock market is very volatile and hard to predict accurately due to the uncertainties affecting stock prices. However, investors and stock traders can only benefit from such models by making informed decisions about buying, holding, or investing in stocks. Also, financial institutions can use such models to manage risk and optimize their customers' investment portfolios. In this paper, we use the Long Short-Term Memory (LSTM-RNN) Recurrent Neural Networks (RNN) to predict the daily closing price of the Amazon Inc. stock (ticker symbol: AMZN). We study the influence of various hyperparameters in the model to see what factors the predictive power of the model. The root mean squared error (RMSE) on the training was 2.51 with a mean absolute percentage error (MAPE) of 1.84%.

The Internet of Things (IoT) is a network of linked items that can gather, exchange, and act on data thanks to sensors, software, and communication technologies. Networking solutions for large-scale IoT deployments have become a cornerstone in advancing smart applications across industries, cities, and healthcare ecosystems. With billions of connected devices generating vast amounts of data, efficient networking infrastructures are required to ensure seamless communication, reliability, and scalability. IoT architectures, ranging from layered models to advanced cloud–edge integration, offer structured approaches for managing heterogeneous devices and diverse communication protocols. Recent innovations, such as 5G connectivity, Software-defined networking and network function virtualization provide flexibility and programmability, enabling dynamic adaptation to evolving requirements. Edge and fog computing further enhance responsiveness by processing data closer to devices, while AI-driven approaches contribute to intelligent routing, predictive analytics, and self-optimizing network management. Blockchain-based frameworks add transparency and trust, securing data exchange in decentralized environments perspective on how networking continues to transform the capabilities of IoT, setting the foundation for sustainable growth and future adoption across multiple domains.

The recent development of Mobile System-on-Chip (SoC) has completely changed the functionality of the contemporary smartphones, wearables, and IoT devices through enhanced computational strength but with stringent power and heat constraints. Mobile applications are becoming more complex (e.g., real-time computer vision, high-resolution imaging, on-device artificial intelligence, etc.) and SoCs have to provide high throughput, good work load distribution, and responsive multitasking without sacrificing battery life. To overcome these issues, modern SoCs are based on heterogeneous multicore designs, with big. LITTLE designs, special purpose accelerators, like NPUs and ISPs, and memory arranged hierarchies and backed with high bandwidth buses. Also, more sophisticated power-management approaches like Dynamic Voltage and Frequency Scaling (DVFS), clock gating, and power gating provide fine-tuning energy management in the active and idle conditions. Most recently, scheduling and thermal sensitivity resource allocation using AI has become a promising approach in increasing real-time refinement and flexibility. This paper gives a detailed discussion of performance-driven and power-efficient design methods in mobile SoCs, including architectural innovations, workload optimization, and built-in AI-assisted energy management. The discussion identifies major research trends, system-level developments, and new challenges defining next-generation mobile computing systems.

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.

Networking has become more common in recent years because it provides structural support and consistent avenues for contact among experts. The purpose of the study was to examine tutors’ and students’ perceptions and sustenance of networking in Food and Nutrition education in the Colleges of Education of Ghana. Quantitatively the study employed a cross-sectional survey research design. The population of the study comprised tutors and students in the Colleges of Education in Ghana offering Food and Nutrition. Purposive, stratified and simple random sampling techniques were used to select colleges of education, 16 tutors and 256 students for the study. The main instrument used for data collection was a questionnaire. The data collected were processed and analysed with the aid of Statistical Package for Social Sciences (SPSS) version 23.0. All statistical analyses were tested at a 5% level of significance. <i>Levene's Test for Equality of Variances</i> was computed to determine the significant difference in the perception of networking in Food and Nutrition education between tutors and students. It can be concluded that both tutors and students are in favour of networking in Food and Nutrition education. The tutors and students believed networking education can foster collaboration, help implement new ideas to improve the quality of teaching, create an environment conducive to teaching and learning; and help students in sharing vital information. Since there is a positive perception on the use of networking, it is recommended that the tutors should foster collaboration, and create a conducive environment to enhance the positive perception and smooth implementation of networking in Food and Nutrition education at Colleges of Education in Ghana. The study indicated that in order to sustain networking education, teachers need to be more cognizant of their interactions and the influence they have on students. It is therefore recommended that teachers maintain contact with students, and ensure a spirit of unity in diversity among the students.

Quantitatively, the study adopted a descriptive research design. The population of this study comprised two thousand (2000) students in the four (4) senior high schools (Edinaman Senior High, Eguafo Senior High, Peter Hold Book Senior High and Komenda Senior Technical Institute) in Komenda Edina Eguafo Abirem municipality. Purposive, simple random and stratified sampling techniques were used to select two hundred students from the four for this study. A questionnaire was the main instrument for data collection. There are more adverse effects of social media network participation on academic performance than positive effects. Social media network sites serve as a useful medium for enhancing students’ academic performance if properly used. Therefore, SHS students should be guided to use social media properly to enhance their academic performance. It is recommended that regular counselling by school authorities and parents for students who participate in social media networks should be done to prevent improper use of social media and avoid addiction and its consequences. It is also recommended that teachers should encourage students to use the right grammar and correct spelling of words when participating in social networks to help stop the negative effect it has on students’ academic performance. It is once again recommended that all stakeholders should be involved in educating students on the proper use of social media networks for their academic work as well as the dangers of improper use on their academic performance and social well-being.

The social aspect of education is an important part of the learning process. In this study two research questions were asked to explore this idea. Social network analysis provided multiple measures of AP Physics 1 students’ network centrality. These measures were used to predict physics achievement. Further, survey results measuring extroversion (EPI, alpha=.84-.94), motivation (PGOS, alpha=.64-.83), and self-efficacy (SOSESC, alpha=.94) were used to determine students’ personality characteristics. These personality results were used as moderators for the moderation analysis. The sample consisted of 106 students from a large, Midwestern, suburban high school taking AP Physics 1. Numerous centrality measures significantly correlated with physics achievement. Extroversion and self-efficacy increased the effect of centrality in most cases, whereas motivation did not change the centrality-achievement relationship. In most cases, having many, high quality connections was beneficial to learning physics, but personality characteristics must also be included in pedagogical decisions. Based on the results, teachers are strongly advised to account for personality and student connections when forming groups.

The effects on the elderly are disproportionately Alzheimer’s disease (AD) is one of the most prevalent and chronic types of dementia. Alzheimer's disease (AD), a fatal illness that can harm brain structures and cells long before symptoms appear, is currently incurable and incurable. Using brain MRI pictures from a publicly accessible Kaggle dataset, this study suggests a prediction model based on Convolutional Neural Networks (CNNs) to help with the early detection of Alzheimer's disease. Four levels of dementia have been applied to the 6,400 photos in the collection: not demented, slightly demented, moderately demented, and considerably mildly demented. Pixel normalization, class balancing utilizing data augmentation techniques, and picture scaling to 128×128 pixels were all part of a thorough workflow for data preparation. To improve the gathering of spatial dependence in volumetric MRI data, a 3D convolutional neural network (CNN) architecture was used. We used important performance measures including F1-score, recall, accuracy, precision, and log loss to gauge the model's effectiveness. A review of the available data indicates that the total F1-score, accuracy, recall, and precision were 99.0%, 99.0%, and 99.38%, respectively. The findings demonstrate the model's potential for practical use in early AD diagnosis and establish its robustness with the help of confusion matrix analysis and performance curves.

Financial markets have become more and more complex, so has been the number of data sources. Stock price prediction has hence become a tough but important task. The time dependencies in stock price movements tend to escape from traditional models. In this work, a hybrid ARIMA-LSTM model is suggested to enhance accuracy of stock price forecasts. Based on time series indicators like adjusted closing prices of S&#x26;P 500 stocks over a decade (2010–2019), the ARIMA-LSTM model combines influences of both autoregressive time series forecasting with the substantial sequence learning property of LSTM. Data preprocessing in all aspects including missing values interpolation, outlier’s detection and data scaling – Min-Max guarantees data quality. The model is trained on 90/10 training/testing split and met with main performance metrics: MaE, MSE &#x26; RMSE. As indicated in the results, the proposed ARIMA-LSTM model gives a MAE value and MSE value of 0.248 and 0.101 respectively and RMSE of 0.319, a measure high accuracy on stock price prediction. Coupled comparative analysis with other Artificial Neural Networks (ANN) and BP Neural Networks (BPNN) are examples of machine learning reference models, further illustrates the suitability and superiority of ARIMA-LSTM approach as compared to the underlying models with the least MAE and strong predictive capability. This work demonstrates the efficiency of integrating the classical time series models with deep learning methods for financial forecasting.

There is a growing body of evidence that the number of individuals suffering from chronic and acute pain is under-reported and the burden of the veteran, aging, athletic, and working populations is rising. Current pain management is limited by our capacity to collaborate with individuals continuing normal daily functions and self-administration of pain treatments outside of traditional healthcare appointments and hospital settings. In this review, the current gap in clinical care for real-time feedback and guidance with pain management decision-making for chronic and post-operative pain treatment is defined. We examine the recent and future applications for predictive analytics of opioid use after surgery and implementing real-time neural networks for personalized pain management goal setting for particular individuals on the path to discharge to normal function. Integration of personalized neural networks with longitudinal data may enable the development of future treatment personalizations paired with electrical simulations.

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.