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Optimal quality design of smart technologies for port digitalization: A game theoretical approach under digitalization synergy
No full textExcerpt: Smart ports improve operational efficiency through innovative technologies and data-driven solutions. A key strategic decision in the digitalization process is the quality level of smart technologies adopted. This paper examines optimal quality design under the landlord port model framework, where a port authority and a terminal operator jointly influence digital transformation. Abstract © Elsevier
Impact of amorphous pockets on displacement damage evolution in silicon
No full textExcerpt: Silicon has long been known to exhibit amorphization in response to heavy particle bombardment. For doses below the total amorphization threshold, partial amorphization is observed in the form of scattered amorphous pockets. While extensive research has gone into modeling the formation and evolution of amorphous pockets in response to irradiation, no studies yet investigate their impact on the evolution of other damage such as interstitial supersaturation and clustering. In this study, we survey the impact of amorphous pockets on defect evolution in silicon when treated as static sinks. Abstract © Elsevier.
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Machine learning approach to synthetic data generation: Uncertainty generative model with neural attention
No full textData scarcity undermines the precision of empirical and analytical research by limiting sample sizes and reducing statistical power. In domains such as business operations, financial management, and information systems, failure data often arise from rare events, introducing substantial aleatoric and epistemic uncertainty. Existing synthetic data generation methods, including interpolation‐based oversampling and generative models, face persistent challenges. They often fail to capture rare events, preserve temporal dependencies, or model multiple sources of uncertainty, leading to unrealistic samples and degraded performance in downstream tasks. This study introduces the uncertainty generative model with neural attention (UGMNA), a synthetic data generation approach that integrates attentive neural processes, the Heston stochastic volatility model, and stochastic differential equations within a continuous‐time latent framework. UGMNA addresses data scarcity by generating synthetic samples that emulate the distributional characteristics of original datasets while explicitly modeling both aleatoric and epistemic uncertainty. Its design enhances statistical power by augmenting limited datasets and ensures that synthetic data reflect key patterns, temporal dynamics, and complex distributions encountered in real‐world scenarios. Experimental results across multiple case studies demonstrate that UGMNA reduces both types of uncertainty while preserving essential data patterns. Compared with conventional baselines and state‐of‐the‐art generators, UGMNA consistently improves predictive accuracy, ranking performance, and model calibration in data‐scarce, high‐variance environments. These findings establish UGMNA as a robust framework for generating reliable synthetic data, offering practical utility for research and decision‐making in contexts where data scarcity and uncertainty hinder model development
Target Defense Using a Turret and Mobile Defender Team
No full textA scenario is considered wherein a stationary, turn constrained agent (Turret) and a mobile agent (Defender) cooperate to protect the former from an adversarial mobile agent (Attacker). The Attacker wishes to reach the Turret prior to getting captured by either the Defender or Turret, if possible. Meanwhile, the Defender and Turret seek to capture the Attacker as far from the Turret as possible. This scenario is formulated as a differential game and solved using a geometric approach. Necessary and sufficient conditions for the Turret-Defender team winning and the Attacker winning are given. In the case of the Turret-Defender team winning equilibrium strategies for the min max terminal distance of the Attacker to the Turret are given. Three cases arise corresponding to solo capture by the Defender, solo capture by the Turret, and capture simultaneously by both Turret and Defender
Impact of Prior Exposures on Biomarkers of Blast during Military Tactical Training
No full textBlast injuries and subclinical effects are of significant concern among those Service Members (SMs) participating in military operations and tactical trainings. Studies of SMs repeatedly exposed during training find concussion-like symptomology with transient decrements in neurocognitive performance, and alterations in blood biomarkers. How prior mild TBI (mTBI) history interacts with low-level blast (LLB) exposure, however, remains unexplored, which we investigate in the present study, to identify interindividual biomarker changes from LLB exposures influenced by prior history of mTBI
Development of a standard maritime \u3ci\u3eC\u3csup\u3e2\u3c/sup\u3e\u3csub\u3en\u3c/sub\u3e\u3ci\u3e model using satellite measurements
No full textQuantifying ambient aerosol absorption and scatter from nano- and micro-particle number concentrations
No full textInfrastructure Planning for Multi-Vehicle Routing with Cooperative Localization
No full textExcerpt: The scope of this paper pertains to the problem of multi-vehicle cooperative navigation in a known environment, where known features are identified for localization