Volume 6.
Issue 09.

Laser-plasma interactions are fundamental to a range of applications, from inertial confinement fusion to advanced material processing. Understanding the linear absorption mechanisms within these interactions is crucial for optimizing performance and predicting outcomes. This study investigates the linear absorption processes that occur when intense laser fields interact with plasmas. Through a combination of theoretical modeling and experimental analysis, we explore how factors such as laser intensity, plasma density, and wavelength influence the absorption characteristics. Our findings reveal key insights into the absorption efficiency and its dependence on plasma parameters, providing a deeper understanding of energy deposition and plasma behavior. The results have significant implications for enhancing the precision of laser-driven systems and improving the efficacy of plasma-based technologies.

This article synthesizes contemporary theory and practice in fault-tolerant microarchitectures with a focus on RISC-V processor cores, dual-core lockstep designs, software-implemented redundancy, and latency-aware provisioning in prediction-serving pipelines. Drawing exclusively on the provided literature, it constructs an integrative perspective that links radiation- and transient-fault mitigation strategies from aerospace and satellite applications to safety-critical domains such as automotive zonal controllers and edge prediction services. The structured abstract presents: (a) background emphasizing the confluence of reliability, performance, and cost pressures; (b) methods describing comparative, design-oriented, and analytical approaches distilled from the references; (c) key thematic results synthesizing hardware-and-software co-design patterns, statistical injection frameworks, thread protection techniques, and system-level provisioning trade-offs; and (d) conclusions outlining research directions, practical recommendations, and limitations. This contribution does not present new empirical measurements but offers a deep theoretical elaboration that clarifies design choices, exposes nuanced trade-offs, and proposes a unified framework for future experimental validation and standardization.

The performance of health care systems is a critical factor influencing public health outcomes, and mortality indices serve as key indicators in evaluating this performance. This study aims to conduct an international comparison of health care systems by analyzing mortality indices across different countries. Utilizing a comprehensive dataset that includes various mortality rates—such as infant mortality, life expectancy, and age-adjusted mortality—the study provides a comparative assessment of health care effectiveness and system efficiency.

We employed statistical methods to analyze mortality data from multiple countries, examining correlations between mortality indices and health care system attributes such as access to care, quality of services, and health expenditures. The analysis reveals significant variations in health care system performance, highlighting differences in mortality outcomes and identifying best practices among high-performing systems.

Key findings include variations in mortality rates attributable to differences in health care policies, resource allocation, and preventive care measures. Countries with higher investment in health care infrastructure and preventive services generally demonstrated better mortality outcomes. The study also identifies areas where improvements can be made, particularly in systems with less favorable mortality indices. This comparative analysis provides valuable insights for policymakers and health care administrators seeking to enhance system performance and improve health outcomes. By understanding the factors that contribute to successful health care systems, countries can adopt effective strategies to reduce mortality rates and achieve better overall public health.

Attrition entails significant costs of hiring, lost productivity, and lost know-how, which drive ML research on employee attrition prediction. Nevertheless, most existing work offers just one discriminatory statistic on one IBM HR Analytics attrition prediction synthetic split, without providing much guidance, and hardly addresses cost, interpretability, dynamics over time, robustness to new data, and fairness altogether. In contrast, this paper proposes a holistic decision-oriented framework and a new targeting policy contributing to attrition analysis in the following six dimensions: (i) cost-sensitive stacked ensemble (LightGBM, CatBoost, logistic regression) with repeated cross-validation, confidence intervals, and expected net savings metric rooted in retention economics; calibration proves indispensable for any cost-sensitive applications; (ii) post hoc explainability based on SHAP(SHapley Additive exPlanations) explanations together with DiCE (Diverse Counterfactual Explanations)counterfactual recourse; (iii) survival analysis (Kaplan-Meier estimator, Cox proportional hazards model) applied to a time-to-event dataset of turnover as another base-classification target; (iv) uplift modeling using three kinds of learners (S-, T-, and X-); (v) Fairness-Aware Cost-Sensitive Retention Targeting policy, FACS-RT, integrating uplift, cost, and fairness optimization in one algorithm and constructing value-fairness Pareto frontier; and (vi) leave-one-department-out resampling and auditing with respect to group fairness criterion. For the IBM dataset (n = 1,470), our approach yields an average AUC of 0.83 with 95% confidence interval (0.79–0.89) with cross-validation, statistically equivalent to a strong logistic regression baseline (paired-bootstrap test p = 0.37), and isotonic calibration brings ECE down to 0.04. For the turnover dataset (n = 1,129), our method achieves AUC 0.72 and Cox concordance 0.66. There is a partial agreement between risk- and uplift-based ranking orders of 27%. FACS-RT retains 83% of expected maximum value while decreasing gender disparity of selection rates by 92% (0.053 → 0.004).

The article presents the results of studying the chemical-mineralogical and physical-mechanical characteristics of Angren secondary kaolin and kaolinite clay and their suitability for obtaining aluminosilicate proppants. It was found that the melting point of Angren secondary kaolin is 1540 ° C and the compressive strength in the fired at a temperature of 1400 ℃ reaches up to 105 MPa, and for kaolinite clay these indicators showed 1505 ℃ and 109 MPa, respectively. The obtained indicators suggest that these clay materials can be used as the main component for obtaining aluminosilicate proppants.