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Communities are increasingly harnessing the coastal protection functions of marshes and other coastal ecosystems within built infrastructure, developing nature-based designs to stabilize coastlines. These “living shorelines” often include planting ecosystem-engineering plants, which have traits that attenuate waves and facilitate sediment accretion while limiting erosion. However, failure is common during plant establishment, requiring interdisciplinary approaches to inform planting designs that enhance short-term sediment stability. Here we combine hydrodynamic modelling with mesocosm experiments to assess different planting approaches for the marsh grass Spartina alterniflora. The model, parameterized with traits measured in the experiments, showed that random arrangement of plants outperformed regular arrangements, reducing areas of high flow velocities and increasing tortuosity, facilitating sediment stability. Furthermore, wide-diameter Spartina clumps with increased biomass reduced flow better than small-diameter clumps, even when the area occupied by the vegetation site-wide is identical. Our experiments revealed multiple factors that influence the diameter and biomass of Spartina clumps, including plant source, sediment characteristics, and spatial arrangement of propagules. While some sources performed better than others, their relative performance varied with time and environment, suggesting that practitioners plant multiple sources to ensure incorporating high-performers in variable and often unexamined planting environments. Furthermore, clumping propagules during planting best generated the large, dense clumps that facilitate sediment stability. 

Abstract A catastrophic Mw7.8 earthquake hit southeast Turkey and northwest Syria on February 6th, 2023, leading to more than 44 k deaths and 160 k building collapses. The interpretation of earthquake-triggered building damage is usually subjective, labor intensive, and limited by accessibility to the sites and the availability of instant, high-resolution images. Here we propose a multi-class damage detection (MCDD) model enlightened by artificial intelligence to synergize four variables, i.e., amplitude dispersion index (ADI) and damage proxy (DP) map derived from Synthetic Aperture Radar (SAR) images, the change of the normalized difference built-up index (NDBI) derived from optical remote sensing images, as well as peak ground acceleration (PGA). This approach allows us to characterize damage on a large, tectonic scale and a small, individual-building scale. The integration of multiple variables in classifying damage levels into no damage, slight damage, and serious damage (including partial or complete collapses) excels the traditional practice of solely use of DP by 11.25% in performance. Our proposed approach can quantitatively and automatically sort out different building damage levels from publicly available satellite observations, which helps prioritize the rescue mission in response to emergent disasters. 

Abstract In nuclear collisions at RHIC energies, an excess of$$\Omega$$ $\Omega$hyperons over$$\bar{\Omega }$$ $\overline{\Omega }$is observed, indicating that$$\Omega$$ $\Omega$has a net baryon number despitesand$$\bar{s}$$ $\overline{s}$quarks being produced in pairs. The baryon number in$$\Omega$$ $\Omega$may have been transported from the incident nuclei and/or produced in the baryon-pair production of$$\Omega$$ $\Omega$with other types of anti-hyperons such as$$\bar{\Xi }$$ $\overline{\Xi }$. To investigate these two scenarios, we propose to measure the correlations between$$\Omega$$ $\Omega$andKand between$$\Omega$$ $\Omega$and anti-hyperons. We use two versions, the default and string-melting, of a multiphase transport (AMPT) model to illustrate the method for measuring the correlation and to demonstrate the general shape of the correlation. We present the$$\Omega$$ $\Omega$-hadron correlations from simulated Au+Au collisions at$$\sqrt{s_\text{NN}} = 7.7$$ $\sqrt{s_{\text{NN}}}=7.7$and$$14.6 \ \textrm{GeV}$$ $14.6\text{GeV}$and discuss the dependence on the collision energy and on the hadronization scheme in these two AMPT versions. These correlations can be used to explore the mechanism of baryon number transport and the effects of baryon number and strangeness conservation on nuclear collisions. 

Internet of Things (IoT) devices have increased drastically in complexity and prevalence within the last decade. Alongside the proliferation of IoT devices and applications, attacks targeting them have gained popularity. Recent large-scale attacks such as Mirai and VPNFilter highlight the lack of comprehensive defenses for IoT devices. Existing security solutions are inadequate against skilled adversaries with sophisticated and stealthy attacks against IoT devices. Powerful provenance-based intrusion detection systems have been successfully deployed in resource-rich servers and desktops to identify advanced stealthy attacks. However, IoT devices lack the memory, storage, and computing resources to directly apply these provenance analysis techniques on the device. This paper presents ProvIoT, a novel federated edge-cloud security framework that enables on-device syscall-level behavioral anomaly detection in IoT devices. ProvIoT applies federated learning techniques to overcome data and privacy limitations while minimizing network overhead. Infrequent on-device training of the local model requires less than 10% CPU overhead; syncing with the global models requires sending and receiving 2MB over the network. During normal offline operation, ProvIoT periodically incurs less than 10% CPU overhead and less than 65MB memory usage for data summarization and anomaly detection. Our evaluation shows that ProvIoT detects fileless malware and stealthy APT attacks with an average F1 score of 0.97 in heterogeneous real-world IoT applications. ProvIoT is a step towards extending provenance analysis to resource-constrained IoT devices, beginning with well-resourced IoT devices such as the RaspberryPi, Jetson Nano, and Google TPU. 

On February 6, 2023, a major earthquake of 7.8 magnitude and its aftershocks caused widespread destruction in Turkey and Syria, causing more than 55,000 deaths, displacing 3 million people in Turkey and 2.9 million in Syria, and destroying or damaging at least 230,000 buildings. Our research presents detailed city-scale maps of landslides, liquefaction, and building damage from this earthquake, utilizing a novel variational causal Bayesian network. This network integrates InSAR-derived change detection with new empirical ground failure models and building footprints, enabling us to (1) rapidly estimate large-scale building damage, landslides, and liquefaction from remote sensing data, (2) jointly attribute building damage to landslides, liquefaction, and shaking, (3) improve regional landslide and liquefaction predictions impacting infrastructure, and (4) simultaneously identify damage degrees in thousands of buildings. For city-scale, building-by-building damage assessments, we use building footprints and satellite imagery with a spatial resolution of approximately 30 meters. This allows us to achieve a high resolution in damage assessment, both in timeliness and scale, enabling damage classification at the individual building level within days of the earthquake. Our findings detail the extent of building damage, including collapses, in Hatay, Osmaniye, Adıyaman, Gaziantep, and Kahramanmaras. We classified building damages into five categories: no damage, slight, moderate, partial collapse, and collapse. We evaluated damage estimates against preliminary ground-truth data reported by the civil authorities. Our results demonstrate the accuracy of our classification system, as evidenced by the area under the curve (AUC) scores on the receiver operating characteristic (ROC) curve, which ranged from 0.9588 to 0.9931 across different damage categories and regions. Specifically, our model achieved an AUC of 0.9931 for collapsed buildings in the Hatay/Osmaniye area, indicating a 99.31% probability that the model will rank a randomly chosen collapsed building higher than a randomly chosen non-collapsed building. These accurate, building-specific damage estimates, with greater than 95% classification accuracy across all categories, are crucial for disaster response and can aid agencies in effectively allocating resources and coordinating efforts during disaster recovery.