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Target Selection Signals Causally Influence Human Perceptual Decision-Making
No full textThe ability to form decisions is a foundational cognitive function which is impaired across many psychiatric and neurological conditions. Understanding the neural processes underpinning clinical deficits may provide insights into the fundamental mechanisms of decision-making. The N2c has been identified as an EEG signal indexing the efficiency of early target selection, which subsequently influences the timing of perceptual reports through modulating neural evidence accumulation rates. Evidence for the contribution of the N2c to human decision-making however has thus far come from correlational research in neurologically healthy individuals. Here, we capitalized on the superior temporal resolution of EEG to show that unilateral brain lesions in male and female humans were associated with specific deficits in both the timing and strength of the N2c in the damaged hemisphere, with corresponding deficits in the timing of perceptual reports contralaterally. The extent to which the N2c influenced clinical deficits in perceptual reporting speed depended on neural rates of evidence accumulation. This work provides causal evidence that the N2c indexes an early, hemisphere-specific process supporting human decision-making. This noninvasive EEG marker could be used to monitor novel approaches for remediating clinical deficits in perceptual decision-making across a range of brain disorders. Copyright © 2025 the authors
A Novel Cloud Energy Consumption Heuristic Based on a Network Slicing–Ring Fencing Ratio
No full textThe widespread adoption of cloud computing has amplified the demand for electric power. It is strategically important to address the limitations of reliable sources and sustainability of power. Research and investment in data centres and power infrastructure are therefore critically important for our digital economy. A novel heuristic for the minimisation of energy consumption in cloud computing is presented. It draws similarities to the concept of “network slices”, in which an orchestrator enables multiplexing to reduce the network “churn” often associated with significant losses of energy consumption. The novel network slicing–ring fencing ratio is a heuristic calculated through an iterative procedure for the reduction in cloud energy consumption. Simulation results show how the non-convex equation optimises power by reducing energy from 10,680 kJ to 912 kJ, which is a 91.46% efficiency gain. In comparison, the Heuristic AUGMENT Non-Convex algorithm (HA-NC, by Hossain and Ansari) reported a 312.74% increase in energy consumption from 2464 kJ to 10,168 kJ, while the Priority Selection Offloading algorithm (PSO, by Anajemba et al.) also reported a 150% increase in energy consumption, from 10,738 kJ to 26,845 kJ. The proposed network slicing–ring fencing ratio is seen to successfully balance energy consumption and computing performance. We therefore think the novel approach could be of interest to network architects and cloud operators
Therapy, Anxiety, and Social Support: The Role of Resilience in an Irish Population
No full textResilience and social support have been found to be protective factors that mitigate against anxiety severity, and therapy is a known practice for reducing anxiety symptomology, There is a lack of generalizable research on how resilience and social support may influence anxiety levels in people who have attended therapy in comparison to those that never attended therapy, controlling for age and gender differences, applying a novel approach of exploring therapy as a moderator. A sample of 134 participants completed a survey containing the Depression Anxiety Stress Scales (DASS-21), the Multidimensional Scale of Perceived Social Support (MSPSS), and the Brief Resilience Scale (BRS). Moderated Hierarchical Multiple Regression. Block 2 significantly predicted anxiety, with this step explaining 21.8% variance resilience with resilience being the strongest predictor (β = -.286, p = <.001) that significantly lowered anxiety. Block 3 only explained 1.9% of variance. Findings suggest that independent of moderation effects, therapy attendance, resilience and perceived social support directly reduce anxiety, which suggests that increasing one’s resilience might be a primary clinical strategy for managing anxiety. This may be done by implementing resilience focused interventions in primary care settings for those who seek therapy for anxiety
Emotional Regulation in Emerging Adults: The Relationships Between Perceived Parental Expectations, Emotional Regulation Strategies and Self-esteem
No full textAims: The current study aimed to examine how individuals’ perceptions of parental expectations influence their emotional regulation and self-esteem, with a focus on differences between the expectations of two different caregivers. This study also explored six distinct ER strategies (suppression, reappraisal, rumination, engagement, relaxation, and distraction) and assessed their association with the study variables.
Method: A questionnaire was administered to participants (n = 140) via social media, which included questions regarding perceived parental expectations, emotional regulation, ER strategies, and self-esteem.
Results: Results indicated that perceived parental expectations, ER strategies, and self-esteem were associated with lower levels of emotional regulation. However, perceived parental expectations were not associated with self-esteem. Together, perceived parental expectations and ER strategies explained 32% of the variance in emotional regulation levels.
Conclusion: These findings provide a greater understanding of the association between perceived parental expectations, emotional regulation, ER strategies, and self-esteem in emerging adults, as well as the gender differences among these study variables. The study challenges the assumption that parental expectations are a predictor of self-esteem. Theoretical implications of the role of perceived parental expectations in shaping emotional regulation are discussed
Enhancing IoT Network Security Through Intrusion Detection Using Machine Learning
No full textWith the increasing growth of Internet of Things (IoT) devices, network infrastructures have become more vulnerable to cyber-attacks. Traditional network security measures often fall short in detecting sophisticated intrusion patterns in real-time, highlighting the need for intelligent and integrated detection systems. This study proposes a machine learning-based approach to enhance IoT network security by leveraging advanced classification models. The process involves data preprocessing, normalisation, and feature selection using mutual information to identify the most impactful attributes. We evaluated several supervised learning algorithms specifically, Decision Trees, Random Forests, and XGBoost using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. To enhance detection performance further, we implemented a soft voting ensemble classifier that combines the strengths of the individual models. The study also focuses on binary classification by distinguishing benign from malicious traffic, simplifying the real-time detection tasks.
The ensemble model demonstrates superior accuracy, robustness, and generalisation, making it a viable solution for modern IoT intrusion detection systems. All experiments and evaluations are conducted using the CIC IoT 2023 dataset, a comprehensive and up-to-date benchmark for IoT security research
Designing and Scaling OPA for PCI-DSS and HIPAA Compliance in AWS
No full textAs the cloud native infrastructure gets more dynamic and complex, the level of difficulty maintaining its compliance with regulatory standards, such as PCI-DSS and HIPAA, pose challenges to DevOps teams. The traditional manual compliance verification methods are known to be time consuming, error prone and are in most cases taking to configuration drifts. This research proposes an automated solution using Terraform for infrastructure provisioning and Open Policy Agent (OPA) for policy enforcement within an AWS CodePipeline-based CI/CD workflow.
The declarative Rego policies stored in version controlled S3 buckets are continuously validated before the infrastructure code is applied to the cloud environment. The performance of the system is evaluated experimentally across different dimensions including formation time, accuracy of compliance, execution timing stagewise, and scalability (Policies of Observation Planning) with the growing number of OPA policies.
Results show that the automated compliance pipeline improves configuration accuracy by more than 30% and minimally reduces formation time (up to 86% improvement) over manual methods. For instance, the validation of 35 policies takes less than 0.3 seconds. This research describes in detail how policy enforcement can be made operational with Terraform and OPA to ensure compliance as well as deployment agility in cloud infrastructure management
Cloud-Based Emotion Recognition and Sleep Time Analysis for Mental Health
No full textThe signs of poor mental health like depression, stress, anxiety can be seen in everyone but stay undetected. This disorder can be connected with sleep cycle which can lead towards emotional instability. On the primary basis emotion recognition models is focused towards facial expression but does not analyze the sleep pattern of a person. This factor is also an important information for detecting the mental health. In this research we will propose cloud-based solution for emotion recognition which will combine facial expression and track the sleep time data so that we can find any early sign in mental health. For tracing the facial emotion, we will use Convolutional Neural Network (CNN’s), Super Vector Machines (SVMs) and Leave-One-Out-Cross-Validation (LOOCV) for model evaluation and performance estimation, and to track the sleep we will use APIs like Fitbit, Google Fit which are cloud based. This will help us to know the morning emotional state and the amount of sleep time the user consumed. With the help of Amazon Rekognition for facial analysis, AWS Lambda for realtime processing, S3 bucket for storage, we will deploy this model on AWS Cloud Infrastructure. In this research we will understand and recognize the emotion along with sleep cycle to increase the accuracy of mental health detection through cloud-based solution. This will help us to understand and recognize the mental health which will reduce the burden on mental health care system. This research presents a novel cloud-based approach that combines sleep time and facial emotion recognition for monitoring mental health risk factor. Keywords: Facial Emotion Recognition, Sleep time tracking, SVMs, LOOCV, Cloud Computing, Mental Health, Convolutional Neural Networks (CNN), AWS
Multi-Cloud Smart Deploy: An AI-Based CI/CD Optimization with Kubernetes Rollback Strategy
No full textWith the rise of cloud-native applications, we are once again faced with new challenges in terms of deployment orchestration, performance optimization, rollback strategies across multi-cloud environment, etc. The research focuses on a motivating problem statement around efficient deployment of containerized applications in multiple environments including GCP, Azure and AWS with effective monitoring and AI driven performance analysis. Continuous integration and continuous deployment. CI/CD pipelines are a must-have in the industry right now, but they are still largely confined to single-cloud scopes. In this article, we focus on innovative multi-cloud CI/CD deployment with Kubernetes cluster roll back integrated with auto AI based evaluation. The solution developed in this project is utilized as a GitHub Actions driven CI/CD pipeline responsible for the dynamic build, test, and deployment of a Django web application to Kubernetes clusters hosted over GCP, Azure, and AWS. Logs collected automatically and merged include post-deployment metrics such as startup time, rollout duration, and resource usage. Their inputs are logged from four machine learning models running inside Azure Machine Learning (Azure ML), which analyze cloud performance according to training accuracy and inference metrics. Under this systems pipeline architecture, intelligent decisions can be made on which cloud provider is best in the various scenarios. The implementation also provides Kubernetes rollback functionality to reverse bad deployments leading to enhancing reliability and service availability. The project enables key DevOps practices and machine learning workflows in a fully automated manner. The results show consistent and correct training through the ML pipeline, and evidence-based cloud selection. Overall, the proposed system provides greater resiliency, better performance visibility, and automatic rollback, as compared to traditional static deploy. Therefore, we strongly encourage the use of the proposed system in industrial multi-cloud deployments. In sum, the system shines on all axes we tested, and we leave more fine-grained resource profiling and predictive scaling for future work. This new effort is a major step forward in automating and intelligently delivering cloud-agnostic applications
Parental Presence and Closeness: Effects on Emotional Intelligence and Social Adjustment in Adulthood
No full textAims: The current study sought to provide a greater understanding of the impact parental presence has on both emotional intelligence and social adjustment, comparing one versus two parent families. This study also examined how self-reported closeness to parents predicts both emotional intelligence and social adjustment comparing one and two-parent families. Method: An online survey was sent to participants through social media which contained demographic questions and three scales. Scales included the Brief Emotional Intelligence scale (BEIS-10), Social Adjustment scale (sas-m), and the Adult Filial Closeness Scale (AFCS). Results: Results showed that emotional intelligence was higher in one parent families compared to two parent families, individuals from two-parent families had higher levels of social adjustment in some subgroups, higher levels of self-reported closeness showed lower levels of emotional intelligence in both one and two-parent families, self-reported closeness influenced different subgroups of social adjustment in one and two parent families. Conclusion: Findings provide a greater understanding of the impact parental presence and closeness has on emotional intelligence and social adjustment. Findings demonstrate the need for further and more precise research to be done in the future, findings have important implications regarding emotional intelligence and social adjustment
Returning to Work After Maternity Leave: Impacts on Productivity, Emotional Well-Being, and Career Adjustment Among Working Mothers in Ireland
No full textThis dissertation examines the impact of returning to work after maternity leave on mothers in Ireland, focusing on productivity, emotional well-being, and career development. The research used a mixed-methods design with an online survey and in-depth interviews to capture organisational supports and personal experiences. The findings show that stigma at work, the cost and lack of childcare, and limited support from managers were the main barriers. These difficulties were strongest for migrant and low-income mothers, who often had fewer resources and less access to help. The study argues that improving mothers' experiences requires more than formal policies: real cultural change in workplaces and stronger state investment in childcare. The results are relevant for employers and policymakers who want to reduce the motherhood penalty and support gender equality in Ireland