AI Driven Predictive Vulnerability Intelligence for Cloud-Native EcosystemsAI Driven Predictive Vulnerability Intelligence for Cloud-Native Ecosystems

This study combines automated DevSecOps procedures with real-time threat intelligence to present an AI-enhanced architecture for proactive vulnerability management in cloud-native clusters.The solution dynamically injects remediation recommendations into Terraform and ArgoCD processes, continuously aggregates CVE data, and uses machine learning models to assess the possibility of exploits. Contextual vulnerability prioritization, autonomous patch deployment, and continuous risk rating are made possible by combining predictive analytics with infrastructure-ascode automation. In comparison to traditional reactive methods, empirical assessments across AWS, Azure, and GCP clusters show an 89% increase in vulnerability detection accuracy and a 73% reduction in mean time to remediation (MTTR). The framework'ssuitability for large-scale, multi-cloud setups is demonstrated by its low operational overhead (<4%) and smooth interaction with GitOps operations.Within dispersed containerized ecosystems, the suggested method pushes cloud security closer to selfhealing, AI-driven resilience, and ongoing compliance.

- Employee burnout threatens organizational sustainability through reduced productivity, compromised mental health, and elevated turnover rates. Early detection remains critical for maintaining workforce stability. We address burnout prediction optimization at PT. XYZ through three advanced machine learning models: Explainable Boosting Machine (EBM), Long Short-Term Memory (LSTM), and Extreme Gradient Boosting (XGBoost). Our methodology incorporates structured data preprocessing, model construction, training protocols, and rigorous performance evaluation. We assessed models using MAE, RMSE, and R² for regression tasks, alongside Accuracy, Precision, Recall, F1-score, Confusion Matrix, Feature Importance, and ROC curves for classification. Cross-validation ensured robust evaluation, with burnout labels derived from established psychosocial factor assessments. Results reveal LSTM's superior performance at 0.99 accuracy, followed by EBM (0.96) and XGBoost (0.95). LSTM demonstrates exceptional capability in identifying subtle burnout patterns, while EBM delivers high interpretability regarding causal factors. These findings offer a data-driven framework for human resource management, enabling precise, proactive intervention through evidence-based decision-making.

Combining Cyproheptadine Hydrochloride With Targeted Muscle Activation Training to Treat Upper Extremity Stroke: A Randomized, Placebo-Controlled Trial

Accurate interpretation of clinical test results is essential for diagnosing and managing complex immunological disorders. We explored the potential of large language models (LLMs) to automate interpretative reports for immunodeficiency and immune competence assessment via quantitative lymphocyte profiling and B-cell subset phenotyping (QLP/BSP). An LLM was fine-tuned using parameter-efficient techniques on a dataset consisting of immunophenotyping test results and corresponding interpretive reports. Model performance was compared against a retrieval-based method and expert pathologist reports. A novel automated evaluation framework assessed both the accuracy of cell population comments and the clinical relevance of generated interpretations. A custom application was built to simulate clinical workflows and measure the impact on pathologist efficiency and accuracy. Fine-tuned LLMs achieved accuracy comparable to expert pathologists in identifying and commenting on abnormal cell type counts and frequencies, in comparison the retrieval-based method exhibited substantial error rates. There was no significant difference between the rate at which abnormalities for cell subtypes were commented on between the LLMs and pathologists. More importantly, LLMs significantly reduced the time required for a single pathologist to finalize reports with a mean reduction in time of 29%. Our results suggest that LLMs hold promise for enhancing efficiency and consistency in the clinical laboratory setting. By automating aspects of interpretive reporting, LLMs can potentially reduce pathologist workload and improve the turnaround time for critical diagnostic information, while requiring expert pathologist oversight.

SummaryThere is potential for negative consequences for the ecological integrity of fire‐dependent ecosystems as a result of inappropriate fire regimes. This can occur when asset (property) protection is prioritised over conservation objectives in burn programs.Optimisation of fire management for multiple objectives is rarely undertaken. Here, we use integer linear programming to identify burn scheduling solutions that will cost‐effectively achieve asset protection and conservation objectives.An approach to burn scheduling that favours a risk‐averse asset protection strategy results in poor conservation outcomes. Conversely, a conservation‐focused approach achieves only modest asset protection benefits. However, when formulated as a multi‐objective problem, good conservation outcomes can be realised with only a small reduction in potential benefits for asset protection.A conservation‐focused approach resulted in substantially more heterogeneity in burns at multiple spatial scales and a marked reduction in mean time since fire among all forest patches relative to an asset protection scenario. This increase in heterogeneity improves ecological integrity, while the resulting reduction in fuel load is beneficial for asset protection.Synthesis and applications. Mathematical optimisation is a powerful framework for informing fire management that improves the prioritisation and scheduling of controlled burns to efficiently achieve management objectives. By quantifying the trade‐offs that exist between the two competing objectives of conservation and asset protection, we demonstrate that compromise solutions can be identified that achieve good outcomes for both objectives. In a transparent and equitable manner, we show that conservation value may be improved within a fire‐dependent ecosystem with only modest concession to asset protection performance. Explicitly evaluating trade‐offs among competing objectives enables managers to identify potentially undesirable outcomes, and facilitate development of preferred solutions. Heterogeneous burning under the auspices of conservation also has the potential to reduce overall fuel loads within the ecosystem and thus its value for asset protection is likely underappreciated.

The rapid escalation of enterprise data complexity, characterized by a projected global data sphere of 175 zettabytes by 2025, has rendered traditional, human-centric data governance models obsolete. This paper introduces the Meta-Cognitive AI Analytics Framework (MCAAF), a novel architectural paradigm that integrates self-reflective capabilities into autonomous data ecosystems. Unlike standard agentic workflows that execute predefined tasks, the MCAAF employs a "Reflection-Action" loop, enabling systems to autonomously evaluate their reasoning, optimize query logic, and adapt schemas in real-time without human intervention. Analysis of deployment data from 2024-2025 reveals that enterprises adopting this framework achieved a 71.6% reduction in Mean Time to Recovery (MTTR) for pipeline incidents and a 30% reduction in operational costs. By leveraging Neuro-Schema Adaptation (NSA) and self-healing architectures, the MCAAF ensures that data infrastructure evolves synchronously with business requirements, maintaining 99.99% availability even during complex structural migrations. This research synthesizes empirical evidence to demonstrate that meta-cognitive capabilities are the critical differentiator for next-generation enterprise resilience.

Pigeons responded in an observing-response procedure in which three fixed-interval components alternated. Pecking one response key produced food reinforcement according to a mixed schedule. Pecking the second (observing) key occasionally replaced the mixed-schedule stimulus with the stimulus correlated with the fixed-interval component then in effect. In Experiment 1, observing was best maintained by stimuli correlated with a reduction in mean time to reinforcement. That finding was consistent with the conditioned-reinforcement hypothesis of observing behavior. However, low rates of observing were also maintained by stimuli not representing delay reduction. Experiment 2 assessed the role of sensory reinforcement. It showed that response rate was higher when maintained by stimuli uncorrelated with reinforcement delay than when the stimuli were correlated with a delay increase. This latter result supports a symmetrical version of the conditioned-reinforcement hypothesis that requires suppression by stimuli correlated with an increase in time to reinforcement. The results were inconsistent with hypotheses stressing the reinforcing potency of uncertainty reduction.

We describe two variance reduction methods for estimating the mean time to failure (MTTF) in Markovian models of highly reliable systems. The first method is based on a ratio representation of the MTTF and employs importance sampling. The second method is based on a hybrid simulation/analytic technique where the number of simulated transitions are reduced by computing partial results analytically. Experiments with a large example show the effectiveness of both techniques for highly reliable systems.

BackgroundManagement of heart failure with reduced ejection fraction (HFrEF) requires timely initiation and up-titration of guideline-directed medical therapy (GDMT). In safety-net hospitals (SNHs), limited health care staff and resources make achievement of optimal medical therapy challenging. Recent studies have shown that medication titration performed by clinical pharmacists can improve outcomes in ambulatory management of HFrEF; however, the impact of these services within an SNH remains unknown. ObjectiveDetermine the impact of integrating clinical pharmacists into a heart failure (HF) clinic on initiation and titration of GDMT within an SNH. MethodsWe performed a single-center retrospective cohort study of patients with HFrEF treated in an ambulatory HF medication titration clinic within an SNH before and after clinical pharmacist integration. Primary outcomes included dose optimization rates of GDMT, time between clinic visits, and time to optimization of GDMT. Exploratory secondary outcomes were all-cause, HF, and cardiovascular acute care service utilization and all-cause, HF, and cardiovascular mortality before and after clinical pharmacist integration up to 6 months after initial clinic visit. ResultsA total of 153 patients with HFrEF were treated. Baseline characteristics in the pre- and postintervention groups were comparable. After clinical pharmacist integration, there was a statistically significant improvement in optimization of renin-angiotensin-aldosterone system inhibitor or hydralazine-nitrate equivalent (82% vs. 94%, P = 0.02). Dose optimization rates of beta-blockers (90% vs. 83%, P = 0.22) and mineralocorticoid receptor antagonists (57% vs. 57%, P > 0.99) were unchanged. There was a statistically significant reduction in mean time between clinic visits (26 vs. 14 days, P < 0.001) and in mean time to optimization of GDMT (88 vs. 45 days, P = 0.002). All-cause mortality was reduced (13% vs. 2%, P = 0.01). ConclusionIn SNHs, where limited health care staff and resources present as barriers to timely initiation and titration of GDMT, integration of clinical pharmacists into HF clinics can serve as a practical solution.

Clean-catch urine is essential in the investigation of an unwell child but can unfortunately be difficult to obtain in nontoilet-trained children. To this end, we compared the difference in time taken to collect clean-catch urine in nontoilet-trained children via the use of point-of-care ultrasound and traditional methods. A single-center randomized controlled trial was conducted at an urban pediatric emergency department, recruiting 80 patients, of which 73 underwent data analyses. Participants were randomized to either the control arm, which consisted of the traditional "watch and wait" method of collecting a clean-catch sample, or to the intervention arm, which used point-of-care ultrasound to assess bladder volume and to stimulate the micturition reflex. The primary outcome measured was the mean time taken to collect a clean-catch urine sample. Eighty patients (ultrasound, n = 41; standard care, n = 39) underwent randomization using a random number generator. Seven patients were removed from final analysis due to loss to follow-up for various reasons. Seventy-three patients (ultrasound, n = 37; standard care, n = 36) underwent statistical analysis. The ultrasound group had a median time to clean-catch urine of 40 minutes (interquartile range, 52) and mean time of 52 minutes (standard deviation, 42), and the control group had a median time of 55 minutes (interquartile range, 81), and mean time of 82 minutes (standard deviation, 90). This reached statistical significance (1-tail t test, P = 0.033). The baseline characteristics were similar between both groups for sex and age distribution; however, the mean ages were significantly different (2-tail t test, P = 0.049) with 8.4 months in the control group, and 12.3 months in the ultrasound group. We found that there was a statistically and clinically significant reduction in mean time taken to collect clean-catch urine in nontoilet-trained children using point-of-care ultrasound compared with the traditional watch and wait method.

This proposal outlines the comprehensive implementation of an AI-powered Credit scoring and loan underwriting system for Savings and Credit Cooperative organizations (SACCO) in Uganda. The system leverages alternative data sources like mobile money transactions, utility payments and behavioral indicators to build accurate and inclusive borrower risk profiles. By integrating advanced machine learning (ML) and generative pre-trained transformer (GPT) models, this system aims to overcome key challenges in traditional credit assessments like limited credit histories, collateral requirements, and subjective decisions which have led to high default rates and financial exclusion The proposed AI model enhances operational efficiency by delivering real time risk evolutions, improving loan turnaround times and supporting proactive risk management. Human oversight is maintained through explainable AI outputs and override mechanisms, aligning with Uganda’s data protection laws and SACCO values. A dedicated AI Steering Committee will oversee ethical governance while comprehensive training and stakeholder engagement will ensure smooth adoption. Custom built for SACCOs, the system is designed for full integration with existing platforms such as the Mobis management information systems, mobile money platforms, credit bureaus and includes agentic monitoring for fraud and early warnings. Ultimately the solution strengthens financial inclusion, improves portfolio quality and empowers SACCOs to deliver faster fairer and more inclusive lending services across Uganda.

As enterprises embrace digital transformation, the adoption of multi-cloud architectures has emerged as a pivotal strategy for achieving scalability, flexibility, and enhanced performance. Multi-cloud environments enable businesses to distribute workloads across different cloud service providers, reducing reliance on a single vendor and mitigating the risks associated with vendor lock-in. The primary advantage of a multi-cloud strategy lies in its ability to optimize resources, enhance disaster recovery capabilities, and enable seamless integration of on-premises and cloud infrastructures. However, the implementation of multi-cloud architectures presents various challenges such as managing complexity, ensuring data security, and optimizing performance across multiple platforms. This paper aims to explore the various facets of multi-cloud architectures, including their design principles, benefits, and the associated risks. Through a comprehensive review of existing literature and case studies, this research investigates the impact of multi-cloud strategies on operational efficiency, cost optimization, and service continuity in modern enterprises. The paper also identifies key best practices for managing multi-cloud environments, including cloud interoperability, security frameworks, and governance models. A significant hypothesis explored in this research is that enterprises adopting multi-cloud architectures experience improved business resilience and operational agility compared to those relying on single-cloud solutions. The findings are based on a systematic analysis of current trends, challenges, and success stories within the multi-cloud space. The results underscore the importance of aligning multi-cloud strategies with organizational goals, emphasizing the need for robust planning, efficient management, and continuous monitoring to fully leverage the potential of multi-cloud environments.

BackgroundMobility impairment is a common disability in multiple sclerosis (MS) and negatively impacts patients’ lives. Clinical studies suggest that fampridine improves motor function in people with MS.PurposeTo assess the effectiveness...

Machine learning (ML) transitioned from a purely academic discipline to an applied field, gaining strategic importance in various industries. Meanwhile, Machine Learning Operations (MLOps) has been widely adopted by enterprises as a comprehensive approach for developing and managing machine learning applications. Despite its advantages, challenges remain. The rising demand for flexibility and scalability has led organizations to embrace multi-cloud and hybrid cloud architectures as preferred solutions. However, the autonomous and distributed nature of modern application development, combined with the complexity of training and deploying machine learning models, makes unified operational management impractical, and this will further affect application quality and efficiency. To address these challenges, this paper proposes a framework to manage model training and deployment in a multi-cloud environment. This framework uses a policy-based resource provisioning approach, agent-based application topology reconstruction, and a visualization dashboard. It aims to provide a cloud provider-neutral solution that enhances the quality of application operations. The framework design is introduced, followed by the implementation of a proof-of-concept prototype. Experiments conducted in various empirical scenarios demonstrate that the proposed framework effectively manages deployment resources while providing clear visibility and control across multiple clouds. The results confirm that this framework enhances control over deployment resources and optimizes model deployment efficiency in multi-cloud infrastructure.

The rapid increase of multi-cloud adoptions and federated data ecosystems has upended the way enterprises manage and protect personally identifiable information. Although such designs enable scalability, flexibility, and business cross-industry cooperation, they are also facing new challenges in the privacy and security area, because of diverse hardware characteristics among countries, data transfer across borders, and different definitions of privacy handling. The old school perimeter-based controls, which worked in static and siloed setups, are pretty useless when you're always distributed, and often AI-driven operations get into gear. Towards that end, this article presents the referred to unified AI-powered Privacy Engineering framework, which incorporates federated learning, differential privacy, zero trust philosophy, and automated governance into the design and operation of new generation “cloud native” systems. The proposed framework will highlight 4 architectural layers: (i) Federated Data Integration - which secure collaboration is enabled without centralizing raw PII, (ii) Privacy-Enhancing Technologies (PETs) such as homomorphic encryption, secure enclave and differential privacy to maintain the confidentiality under distributed processing; (iii) AI-Assisted Governance and Compliance - a intelligent policy orchestration automates regulatory alignment with real-time data lineage tracking; and (iv) Zero-Trust Adaptive Security - that it necessitates continual verification and anomaly detection on multi-cloud environments. With the infusion of AI on every layer, the framework evolves from its original reactive compliance enforcement to proactive context-aware privacy management. The empirical validation is executed through cases in healthcare and finance. In healthcare, federated oncology models showed that PII leakage was reduced by 38% with performance comparable to near-baseline, and compliance report time went down by 42%. That is, we negotiated a 25% latency reduction in detecting anomalies for a global bank’s fraud detection pipeline and a 40% improvement in cross-border audit readiness in the financial domain. Benchmark analysis also reveals that AI-enabled privacy engineering brings: 25% reduction in integration errors; 30-40% speedup to secure deployment; and 20% increase in throughput for federated workflows. The findings demonstrate both the technical feasibility and the strategic necessity of AI-driven privacy engineering at a time when regulatory mandates, adversarial AI risks, and pressures from cross-border data sharing have converged. The results suggest that building privacy into the architecture of systems is now a necessity to maintain resilience, compliance, and trust in multi-cloud and federated ecosystems