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Modern container ships face higher wind loads due to increased windage areas, making accurate predictions of wind loads essential for mooring design. Existing empirical models, largely developed for container ships with smaller windage areas and simpler geometrical configurations than those of modern large-scale vessels, often lack accuracy and do not account for the influence of nearby structures. This study proposes a multi-fidelity surrogate modelling framework for the prediction of wind-load coefficients, combining empirical correlations with simplified and detailed CFD models for ships in open-sea and harbor environments. The approach relies on recursive co-kriging to consistently fuse information across fidelity levels, enabling accurate predictions at a reduced computational cost. A sensitivity analysis is used to identify the most influential geometric parameters, and the resulting reduced parameter space is explored through sequential sampling to efficiently construct the training database. The surrogate models are validated over a wide range of loading configurations and for two distinct harbor environments. The results demonstrate that the multi-fidelity approach signifi

Giant radio sources (GRSs) harbor the Universe's largest structures generated by individual galaxies, with projected source sizes exceeding 700 kpc. These enigmatic objects have been mainly studied at radio frequencies, and their physical properties in the high-energy domain are poorly understood. Here we present the results of a multiwavelength study focused on NuSTAR J112829+5831.8 (J1128+5831), the only known GRS serendipitously detected with the Nuclear Spectroscopic Telescope Array. Being located in proximity to the famous interacting galaxy system, Arp 299, J1128+5831 has been serendipitously observed also by the Chandra X-ray Observatory, Hubble Space Telescope, and XMM-Newton satellites. From radio observations with the Low Frequency Array, the NRAO VLA Sky Survey and the Very Large Array Sky Survey, we have determined that J1128+5831 has an overall steep radio spectrum ($α=-0.86$; $F_ν\proptoν^α$) and a low core dominance ($C_{\rm D}=-2.4$, in log-scale), indicating the source to be viewed at large angles. From the X-ray spectral analysis, we found J1128+5831 to harbor an obscured active galactic nucleus (AGN) with neutral hydrogen column density exceeding $10^{23}$ cm$^{-

Behavioral healthcare risk assessment remains a challenging problem due to the highly multimodal nature of patient data and the temporal dynamics of mood and affective disorders. While large language models (LLMs) have demonstrated strong reasoning capabilities, their effectiveness in structured clinical risk scoring remains unclear. In this work, we introduce HARBOR, a behavioral health aware language model designed to predict a discrete mood and risk score, termed the Harbor Risk Score (HRS), on an integer scale from -3 (severe depression) to +3 (mania). We also release PEARL, a longitudinal behavioral healthcare dataset spanning four years of monthly observations from three patients, containing physiological, behavioral, and self reported mental health signals. We benchmark traditional machine learning models, proprietary LLMs, and HARBOR across multiple evaluation settings and ablations. Our results show that HARBOR outperforms classical baselines and off the shelf LLMs, achieving 69 percent accuracy compared to 54 percent for logistic regression and 29 percent for the strongest proprietary LLM baseline.

Long-horizon language-model agents are dominated, in lines of code and in operational complexity, not by their underlying model but by the harness that wraps it: context compaction, tool caching, semantic memory, trajectory reuse, speculative tool prediction, and the glue that binds the model to a sandboxed execution environment. We argue that harness design is a first-class machine-learning problem and that automated configuration search dominates manual stacking once the flag space exceeds a handful of bits. We defend this claim in two steps. First, we formalize automated harness optimization as constrained noisy Bayesian optimization over a mixed-variable, cost-heterogeneous configuration space with cold-start-corrected rewards and a posterior chance-constrained safety check, and give a reference solver, HARBOR (Harness Axis-aligned Regularized Bayesian Optimization Routine), built from a block-additive SAAS surrogate, multi-fidelity cost-aware acquisition, and TuRBO trust regions. Second, we instantiate the problem in a flag-gated harness over a production coding agent and report a controlled four-round manual-tuning case study against a fixed task suite and an end-to-end HARBO

Infrastructure inspection is becoming increasingly relevant in the field of robotics due to its significant impact on ensuring workers' safety. The harbor environment presents various challenges in designing a robotic solution for inspection, given the complexity of daily operations. This work introduces an initial phase to identify critical areas within the port environment. Following this, a preliminary solution using a quadruped robot for inspecting these critical areas is analyzed.

Maritime situational awareness often relies on Automatic Identification System (AIS) transmissions to track vessel movements. However, in operational or conflict scenarios, these data may be unavailable due to signal loss, deliberate deactivation, or intentional spoofing. In such conditions, synthetic aperture radar (SAR) imagery becomes a critical sensing alternative for wide-area maritime monitoring, despite providing only static scene snapshots. This work introduces HARBOR (Heading Analysis and Reconstruction from Behavioral Observation and Radar), a complete pipeline for transforming a single SAR image into predictive motion information without requiring any auxiliary data source at inference time. The method begins with SAR image preprocessing to enhance and segment vessel candidates, followed by automatic detection, size-based classification, and heading estimation using skeleton geometry and local intensity patterns. AIS data are used exclusively during an offline calibration phase to derive vessel-type-dependent motion parameters, which are then applied to generate probabilistic heatmaps of candidate future vessel positions. A case study using real COSMO-SkyMed SAR imagery

In this paper, we investigate the hydrodynamic characteristics of harbor seal locomotion, focusing on the role of hind flippers in thrust generation and wake dynamics. Through three-dimensional numerical simulations using an immersed boundary method at Reynolds number of 3000, we analyze the impact of varying Strouhal number (St = 0.2-0.35) and propulsive wavelength ($λ^\ast = 1.0-1.2$) on swimming performance. Our findings reveal two distinct wake patterns: a single-row structure at lower Strouhal numbers ($St \leq 0.25$) and a double-row configuration at higher St ($St \geq 0.3$). Increasing wavelength generally enhances thrust production by reducing both pressure and friction of drag components. Additionally, we identify critical vortex interactions between the front and hind flippers, with destructive interference occurring at lower St and constructive patterns emerging at higher St. Circulation analysis confirms stronger vortex formation at higher St and $λ^\ast$}, particularly during the left stroke phase. These results provide novel insights into the hydrodynamic mechanisms underlying seal locomotion and contribute to our understanding of efficient aquatic propulsion systems

When deploying artificial skills, decision-makers often assume that layering human oversight is a safe harbor that mitigates the risks of full automation in high-complexity tasks. This paper formally challenges the economic validity of this widespread assumption, arguing that the true bottom-line economic utility of a human-machine skill policy is highly contingent on situational and design factors. To investigate this gap, we develop an in-silico exploratory framework for policy analysis based on Monte Carlo simulations to quantify the economic impact of skill policies in the execution of tasks presenting varying levels of complexity across diverse setups. Our results show that in complex scenarios, a human-machine strategy can yield the highest economic utility, but only if genuine augmentation is achieved. In contrast, when failing to realize this synergy, the human-machine approach can perform worse than either the machine-exclusive or the human-exclusive policy, actively destroying value under the pressure of costs that are not sufficiently compensated by performance gains. This finding points to a key implication for decision-makers: when the context is complex and critical,

We investigate factors contributing to LLM agents' success in competitive multi-agent environments, using auctions as a testbed where agents bid to maximize profit. The agents are equipped with bidding domain knowledge, distinct personas that reflect item preferences, and a memory of auction history. Our work extends the classic auction scenario by creating a realistic environment where multiple agents bid on houses, weighing aspects such as size, location, and budget to secure the most desirable homes at the lowest prices. Particularly, we investigate three key questions: (a) How does a persona influence an agent's behavior in a competitive setting? (b) Can an agent effectively profile its competitors' behavior during auctions? (c) How can persona profiling be leveraged to create an advantage using strategies such as theory of mind? Through a series of experiments, we analyze the behaviors of LLM agents and shed light on new findings. Our testbed, called HARBOR, offers a valuable platform for deepening our understanding of multi-agent workflows in competitive environments.

For controlling pollution of the marine environment while developing coastal economy, the coastal environmental performance was proposed and measured in static and dynamic methods combined with DEA and efficiency theory in this paper. With the two methods, 16 harbor cities were evaluated. The results showed the index designed in this paper can better reflect the effect to the marine environment for economy of the coastal cities.

Relativistic jets manifest some of the most intriguing activities in the nuclear regions of active galaxies. Identifying the most powerful relativistic jets permits us to probe the most luminous accretion systems and, in turn, the most massive black holes. This paper reports the identification of one such object, PMN J1310$-$5552 ($z=1.56$), a blazar candidate of uncertain type detected with the Fermi Large Area Telescope (LAT) and Swift Burst Alert Telescope. The detection of broad emission lines in its optical spectra taken with the X-Shooter and Goodman spectrographs classifies it to be a flat-spectrum radio quasar. The analysis of the Goodman optical spectrum has revealed PMN J1310$-$5552 harbors a massive black hole (log scale $M_{\rm BH}=9.90\pm0.07$, in $M_{\odot}$) and luminous accretion disk (log scale $L_{\rm disk}=46.86\pm0.03$, in erg s$^{-1}$). The fitting of the observed big blue bump with the standard accretion disk model resulted in the log scale $M_{\rm BH}=9.81^{+0.19}_{-0.20}$ (in $M_{\odot}$) and $L_{\rm disk}=46.86^{+0.09}_{-0.09}$ (in erg s$^{-1}$), respectively. These parameters suggest PMN J1310$-$5552 hosts one of the most massive black holes and the most l

Independent evaluation and red teaming are critical for identifying the risks posed by generative AI systems. However, the terms of service and enforcement strategies used by prominent AI companies to deter model misuse have disincentives on good faith safety evaluations. This causes some researchers to fear that conducting such research or releasing their findings will result in account suspensions or legal reprisal. Although some companies offer researcher access programs, they are an inadequate substitute for independent research access, as they have limited community representation, receive inadequate funding, and lack independence from corporate incentives. We propose that major AI developers commit to providing a legal and technical safe harbor, indemnifying public interest safety research and protecting it from the threat of account suspensions or legal reprisal. These proposals emerged from our collective experience conducting safety, privacy, and trustworthiness research on generative AI systems, where norms and incentives could be better aligned with public interests, without exacerbating model misuse. We believe these commitments are a necessary step towards more inclusi

A dynamic model for an automatic berthing and unberthing controller has to estimate harbor maneuvers, which include berthing, unberthing, approach maneuvers to berths, and entering and leaving the port. When the dynamic model is estimated by the system identification, a large number of tests or trials are required to measure the various motions of harbor maneuvers. However, the amount of data that can be obtained is limited due to the high costs and time-consuming nature of full-scale ship trials. In this paper, we improve the generalization performance of the dynamic model for the automatic berthing and unberthing controller by introducing data augmentation. This study used slicing and jittering as data augmentation methods and confirmed their effectiveness by numerical experiments using the free-running model tests. The dynamic model is represented by a neural network-based model in numerical experiments. Results of numerical experiments demonstrated that slicing and jittering are effective data augmentation methods but could not improve generalization performance for extrapolation states of the original dataset.

A simulation environment of harbor maneuvers is critical for developing automatic berthing. Dynamic models are widely used to estimate harbor maneuvers. However, human decision-making and data analysis are necessary to derive, select, and identify the model because each actuator configuration needs an inherent mathematical expression. We proposed a new dynamic model for arbitrary configurations to overcome that issue. The new model is a hybrid model that combines the simplicity of the derivation of the Taylor expansion and the high degree of freedom of the MMG low-speed maneuvering model. We also developed a method to select mathematical expressions for the proposed model using system identification. Because the proposed model can easily derive mathematical expressions, we can generate multiple models simultaneously and choose the best one. This method can reduce the workload of model identification and selection. Furthermore, the proposed method will enable the automatic generation of dynamic models because it can reduce human decision-making and data analysis for the model generation due to its less dependency on the knowledge of ship hydrodynamics and captive model test. The pro

Over the past few decades, floods have become one of the costliest natural hazards and losses have sharply escalated. Floods are an increasing problem in urban areas due to increased residential settlement along the coastline and climate change is a contributing factor to this increased frequency. In order to analyze flood risk, a model is proposed to identify the factors associated with increased flooding at a local scale. The study area includes National Harbor, MD, and the surrounding area of Fort Washington. The objective is to assess flood risk due to an increase in sea level rise for the study area of interest. The study demonstrated that coastal flood risk increased with sea level rise even though the predicted level of impact is fairly insignificant for the study area. The level of impact from increased flooding is highly dependent on the location of the properties and other topographic information.

Gravitational redshift is a fundamental parameter that allows us to determine the mass-to-radius ratio of compact stellar objects, such as black holes, neutron stars, and white dwarfs (WDs). In the X-ray spectra of the close binary system, RX J1712.6$-$2414, obtained from the Chandra High-Energy Transmission Grating observation, we detected significant redshifts for characteristic X-rays emitted from hydrogen-like magnesium, silicon ($ΔE/E_{\rm rest} \sim 7 \times 10^{-4}$), and sulfur ($ΔE/E_{\rm rest} \sim 15 \times 10^{-4}$) ions, which are over the instrumental absolute energy accuracy (${ΔE/E_{\rm rest} \sim 3.3} \times 10^{-4}$). Considering some possible factors, such as Doppler shifts associated with the plasma flow, systemic velocity, and optical depth, we concluded that the major contributor to the observed redshift is the gravitational redshift of the WD harbored in the binary system, which is the first gravitational redshift detection from a magnetic WD. Moreover, the gravitational redshift provides us with a new method of the WD mass measurement by invoking the plasma-flow theory with strong magnetic fields in close binaries. Regardless of large uncertainty, our new me

Despite not having a clear meaning, public perception and awareness makes the term cyber Pearl Harbor an important part of the public discourse. This paper considers what the term has meant and proposes its decomposition based on three different aspects of the historical Pearl Harbor attack, allowing the lessons from Pearl Harbor to be applied to threats and subjects that may not align with all aspects of the 1941 attack. Using these three definitions, prior attacks and current threats are assessed and preparation for and response to cyber Pearl Harbor events is discussed.