Impact Event Research Articles

New Impacts on Mars: Systematic Identification and Association With InSight Seismic Events

AbstractThe InSight lander represents a unique opportunity to correlate seismic data with impact events identified in orbital images, enabling the characterization of the physical properties of the martian crust and mantle. Here, we present the first comprehensive catalog of impacts that occurred during the InSight mission within a 50° radius around the lander. We use a machine learning‐enabled approach to identify 123 date‐constrained impacts with diameters between ∼1 and 22.5 m. We estimate an impact rate of 2.7 × 10−6/km2/year for >3.9 m effective diameter, which is ∼1.6–∼2.5 times higher than previously derived for Mars. We identify 49 seismic events with one or several potential impact match(es) including a 21.5 m crater located near Cerberus Fossae. Our catalog will enable a more accurate characterization of the propagation of seismic body waves at intermediate distances to InSight (5–50°), with major implications for estimates of other seismic event distances.

Herding unmasked: Insights into cryptocurrencies, stocks and US ETFs.

Herding behavior has become a familiar phenomenon to investors, with potential dangers of both undervaluing and overvaluing assets, while also threatening market stability. This study contributes to the literature on herding behavior by using a recent dataset, covering the most impactful events of recent years. To our knowledge, this is the first study examining herding behavior across three different types of investment vehicle and also the first study observing herding at a community (subset) level. Specifically, we first explore this phenomenon in each separate type of investment vehicle, namely stocks, US ETFs and cryptocurrencies, using the Cross-Sectional Absolute Deviation model. We find mostly similar herding patterns for stocks and US ETFs. Subsequently, the same experiment is implemented on a combination of all three investment vehicles. For a deeper investigation, we adopt graph-based techniques including the Minimum Spanning Tree and Louvain community detection to partition the combination into smaller subsets to detect herding behavior for each subset. We find that herding behavior exists at all times across all types of investment vehicle at a subset level, although perhaps not at the superset level, and that this herding behavior tends to stem from specific events that solely impact that subset of assets. Lastly, we explore herding by examining the financial contagion effects between these types of investment vehicle. Results show that US ETFs not only have a tendency to propagate similar trading behaviors in stocks and especially cryptocurrencies but also show self-reinforcing herding behavior, acting as drivers of their own trends.

Psychological health status after major trauma across different levels of trauma care: A multicentre secondary analysis.

Concentration of trauma care in trauma network has resulted in different trauma populations across designated levels of trauma care. Describing psychological health status, by means of the impact event scale (IES) and the hospital anxiety and depression scale (HADS), of major trauma patients one and two years post-trauma across different levels of trauma care in trauma networks. A multicentre retrospective cohort study was conducted. aged ≥ 18 and an Injury Severity Score (ISS) > 15, surviving their injuries one year after trauma. Psychological health status was self-reported with HADS and IES. Subgroup analysis, univariate, and multivariable analysis were done on level of trauma care and trauma region for HADS and IES as outcome measures. Psychological health issues were frequently reported (likely depressed n = 31, 14.7 %); likely anxious n = 32, 15.2 %; indication of a post-traumatic stress disorder n = 46, 18.0 %). Respondents admitted to a level I trauma centre reported more symptoms of anxiety (3, P25-P75 1-6 vs. 5, P25-P75 2-9, p = 0.002), depression (2, P25-P75 1-5 vs. 5, P25-P75 2-9, p < 0.001), and post-traumatic stress (6, P25-P75 0-15 vs. 13, P25-P75 3-33, p = 0.001), than patients admitted to a non-level I trauma centre. Differences across trauma regions were reported for depression (3, P25-P75 1-6 vs. 4, P25-P75 2-10, p = 0.030) and post-traumatic stress (7, P25-P75 0-18 vs. 15, P25-P75 4-34, p < 0.001). Major trauma patients admitted to a level I trauma centre have more depressive, anxious, and post-traumatic stress symptoms than when admitted to a non-level I trauma centre. These symptoms differed across trauma regions, indicating populations differences. Level of trauma care and trauma region are important when analysing psychological health status.

Location of NWA 6148 and NWA 10153 in the nakhlite body and their aqueous alteration

AbstractThe petrologic and mineralogical characteristics and alteration processes of the nakhlites NWA 6148 and NWA 10153 were studied. Both consist of augite, olivine, and mesostasis. Based on the characteristics of each volume fraction of the components and the chemical composition of olivine and pyroxene, NWA 6148 correspond to lava units crystallized at 1346–1391 Ma in the nakhlite body. The position of NWA 10153 in the nakhlite body is unclear. Iron oxides/hydroxides, barite, and calcite fill the fractures of NWA 6148, which are terrestrial weathering products. In NWA 10153, olivine grains are replaced by goethite, magnetite, saponite, amorphous silica, jarosite, and siderite. Although it is uncertain whether all of the alteration minerals were formed on the surface of Mars or on the surface of Earth, NWA 10153 records two different alteration environments: reducing, neutral to alkaline, and oxidizing and acidic. As in NWA 6148 and NWA 10153, the assemblage of alteration mineral species in other nakhlites is also heterogeneous even within the same lava unit. The nakhlite body was altered by the oxidizing acidic fluid after a ‐bearing reducing neutral to alkaline fluid. The drastic change of alteration environments may have been caused by an impact event.

Evaluating sensor performance for impact identification in composites: a comprehensive comparison of FBGs with PZTs

Aerospace composite components require effective monitoring techniques to detect possible internal damage from impact events. To ensure reliable impact identification, sensor measurements can provide valuable information about impact energy and identify potential issues that may require further investigation. However, selecting the most appropriate sensor technology to measure impact force and energy is a challenge. In this article, a systematic and structured approach is presented to compare the expected performance of sensors and their metrological parameters in terms of their ability for impact identification in aerospace composites. The proposed methodology is demonstrated using an application example where fibre Bragg grating (FBG) are compared with piezoelectric (PZT) sensors through comprehensive tests. These tests include the correlation test, the sensitivity test, and the factor test. The correlation test showed a high agreement between FBG and PZT sensors in the time and frequency domain. The sensitivity test indicated a significant correlation between the signal features and the impact energy levels in the energy profiling diagrams, revealing nonlinearities and energy losses indicative of damage. Furthermore, these results emphasise the superior resolution of the FBG sensors and the comparable repeatability of the two sensor types. Finally, the factor test showed that FBG sensors are sensitive to different angles of incidence, while PZT sensors have a more stable directivity. Further analysis also showed that the signal strength of both sensor types decreases with increasing distance from the impact source. Overall, the proposed approach enables a thorough evaluation of the capabilities and limitations of both sensor types. Consequently, it provides information to make an informed decision on the most suitable sensor for impact monitoring systems.

The diagnostic accuracy of EyeGuide Focus testing for concussion in elite male Rugby players

ABSTRACT Introduction Smooth pursuit eye movements may be affected by head impacts. The EyeGuide Focus system is a simple, portable, test of visual tracking. This study investigated the diagnostic accuracy of EyeGuide measurements for detection of concussion during elite Rugby matches. Methods A prospective diagnostic case–control study was performed in the elite 2021/2022 United Rugby Championship competition. The study population comprised consecutive players identified with match-related head impact events during the World Rugby Head Injury Assessment process, randomly chosen uninjured players, and players with match-related musculoskeletal injuries. The index test was blinded EyeGuide assessment performed by independent assessors. The reference standard was concussion diagnosed by the team doctor. Distributions of EyeGuide scores were compared between concussed and non-concussed players and receiver operator characteristic curves constructed. Results EyeGuide testing was performed in 262 cases, comprising 55 concussed players and 207 non concussed players (33 head impact events, 97 uninjured controls, and 79 musculoskeletal injury controls). The distributions of EyeGuide score were similar between concussed and non-concussed cases (medians 20,120 Vs 21,522, p = 0.3; difference −1,402, 95% CI −5,332–3,865). The c-index for the receiver operating characteristic curve was 0.46 (95% CI 0.36–0.55). Conclusions EyeGuide Focus scores did not appear to discriminate between concussed and non-concussed players in a cohort of elite Rugby players.

Reduced contribution of sulfur to the mass extinction associated with the Chicxulub impact event

The Chicxulub asteroid impact event at the Cretaceous-Paleogene (K-Pg) boundary ~66 Myr ago is widely considered responsible for the mass extinction event leading to the demise of the non-avian dinosaurs. Short-term cooling due to massive release of climate-active agents is hypothesized to have been crucial, with S-bearing gases originating from the target rock vaporization considered an important driving force. Yet, the magnitude of the S release remains poorly constrained. Here we empirically estimate the amount of impact-released S relying on the concentration of S and its isotopic composition within the impact structure and a set of terrestrial K-Pg boundary ejecta sites. The average value of 67 ± 39 Gt obtained is ~5-fold lower than previous numerical estimates. The lower mass of S-released may indicate a less prominent role for S emission leading to a milder impact winter with key implications for species survival during the first years following the impact.

Using X‐ray computed microtomography (μCT) to determine subsample‐specific cosmogenic noble gas production rates of E (enstatite) chondrites

AbstractCosmic ray exposure (CRE) ages provide information about the parent bodies and source regions of meteorite classes. Cosmogenic noble gases are often used to quantify exposure time scales ranging from tens of ka to hundreds of Ma. The production rate of cosmogenic noble gases is primarily controlled by a meteorite's chemical composition. Historically, an average chemical composition for an entire meteorite class or subgroup was used to calculate production rates. At the scale needed for noble gas measurements, however, some meteorites exhibit mineral abundance variabilities that translate into chemical heterogeneities, necessitating subsample‐specific production rates. We find that the metal and sulfide content can vary significantly between ~100 and 300 mg subsamples of the same enstatite (E) chondrite, leading to &gt;10% differences in cosmogenic 21Ne production rates between subsamples. We demonstrate an approach to determining subsample‐specific production rates using E chondrites. We use electron microprobe analysis and X‐ray computed microtomography to quantify the chemical composition and abundances, respectively, of metal, sulfide, and silicate minerals in six E chondrites and calculate subsample‐specific production rates of 3He and 21Ne. By applying this method to more E chondrite subsamples alongside noble gas measurements, we may begin to address broader questions, such as whether peaks in the E chondrite CRE age distribution can be attributed to distinct impact events.

Machine learning-driven power prediction in continuous extrusion of pure titanium for enhanced structural resilience under extreme loading

The increasing demand for advanced materials capable of withstanding extreme loading conditions, such as those encountered during impact or blast events, underscores the need for innovative approaches in material processing. This study focuses on leveraging machine learning (ML) to enhance predictive accuracy in the continuous extrusion of CP-Titanium Grade 2, a material vital for structural resilience in critical applications. Specifically, an Artificial Neural Network (ANN) model optimized using Stochastic Gradient Descent (SGD) was introduced to forecast power requirements with high precision. The analysis utilized a published dataset that comprises theoretical, numerical, and experimental power calculations as a robust foundation for validation and comparison. A visualization highlighted the influence of process parameters, such as feedstock temperature and extrusion wheel velocity, on structural performance to align with the thematic focus of resilient material design. The ANN-SGD model achieved an RMSE of 0.9954 and a CVRMSE of 11.53% which demonstrated significant improvements in prediction accuracy compared to traditional approaches. By achieving superior alignment with experimental results, the model validated its efficacy as a reliable and efficient tool for understanding and optimizing complex manufacturing processes. This research emphasizes the potential of ML to revolutionize material processing for extreme conditions and contribute to the broader goals of structural resilience and sustainable manufacturing.

Revised chronology and expanded insights: Geologic perspective on the Luna impact event and its influence on the Harappan Civilization

Revised chronology and expanded insights: Geologic perspective on the Luna impact event and its influence on the Harappan Civilization

Drop‐Weight Impact Analysis of Unidirectional Sisal Fiber –Reinforced Epoxy Resin Composite Material for Automotive Application Based on ASTM D‐7136 Standard

The main goal of this study is to investigate the drop‐weight impact analysis of sisal fiber–reinforced epoxy resin composite materials for automotive applications using Abaqus/CAE software based on the ASTM D‐7136 standard. For this simulation, a [0/0]s unidirectional sisal/epoxy composite laminate with an overall thickness of 2.16 mm was used, and 0.5, 1, 2, and 3 kg of a rigid hemispherical impactor mass with 1, 2, and 3 m/s of velocities were used to deal with their effects on the composite laminates. The result revealed that the initial damage mechanism in the impact event is the interplay matrix crack. After that, matrix and fiber debonding, fiber breakage, and penetration occurred depending on the mass and velocity of the impactor. In addition, when the impactor mass and velocity increase, the composite plate’s reaction forces and energy absorption increase up to 1 kg of the impactor at 3 m/s. However, for 2 and 3 kg of the impactor mass, the energy absorption capacity of the material is decreased due to the high damage and perforation of the composite laminate. In general, it is found that the unidirectional sisal fiber–reinforced epoxy resin composite material has a moderate energy absorption capacity in the transverse direction and has the potential to be used for different automotive interior body applications.

Numerical study of low-velocity impact performance of hybrid carbon/Twaron woven fabric

In the present study, a finite element impact model of plain-woven fabric was developed and analyzed using commercial FEM code ANSYS® and then validated via a drop-weight impact experiment. Then numerical analysis was carried out to investigate the low-velocity impact performance of hybrid carbon/Twaron fabric. During the analysis, several factors were considered, such as different hybrid ratios of plain fabric, hybrid fabric with different weaves, and the case of a hybrid multi-layer fabric panel. The results show that among the different types of hybrid plain fabric, the impact scenario of plain fabric with a hybrid ratio of 1:1, where carbon yarns are only in the warp direction and Twaron yarns are only in the weft direction (M4-2), has the best impact resistance performance. The impact scenario M4-2 performed excellently in other weaves, such as twill weave, while impact scenario M4-1 (plain fabric with a hybrid ratio of 1:1, one carbon yarns interval with one Twaron yarn) in basket weave showed high suitability for body armor in terms of impact protection. Additionally, arranging Twaron fabric in the top layers of a hybrid multi-layer carbon/Twaron fabric panel yielded superior impact resistance performance during low-velocity impact events compared to other layer arrangements.

Historical rainstorm in Hong Kong on 7–8 September 2023: Diagnosis, forecasting and nowcasting

AbstractOn 7–8 September 2023, Hong Kong was hit by a historical and record‐breaking rainstorm associated with the remnant of Tropical Cyclone Haikui (2311). The hourly rainfall recorded at the Hong Kong Observatory Headquarters once reached 158.1 mm, the highest since record began in 1884. The 24‐h rainfall even exceeded 600 mm in some parts of the territory. The historical rainstorm resulted in heavy flooding and landslides, bringing significant societal impact to Hong Kong. This paper aims to review this unprecedented heavy rain event from the aspects of diagnosis, forecasting and nowcasting. Early indicators of such events over Hong Kong with substantial lead time are limited from the dynamics and thermodynamics consideration, the numerical weather prediction models, given the present technology. The only indication may come from the climatologically extreme total precipitable water. While recent research of developing a regional risk‐based alerting system on the higher impact event of flooding associated with heavy rain might have potential to enhance the weather service, and emerging AI model showed some promising post‐simulations, predicting historical and record‐breaking rainstorms remains a challenge for operational weather forecasting and warning services.

Physical-informed machine learning framework for outage prediction against high impact events to enhance grid resilience

Physical-informed machine learning framework for outage prediction against high impact events to enhance grid resilience

A review of recent advancements in the impact response of fiber metal laminates.

Fiber metal laminates (FMLs) have garnered significant attention due to their exceptional impact resistance, making them attractive for various structural applications. This review presents recent advancements in understanding the impact behavior of FMLs under low- and high-velocity impact scenarios. Low-velocity impacts, commonly encountered during manufacturing, handling, and tool drops, are discussed, with a focus on damage mechanisms, energy absorption capabilities, and influential factors such as impactor geometry and boundary conditions. Additionally, this review delves into high-velocity impact events, simulating scenarios such as ballistic impacts, highlighting the role of FMLs in mitigating perforation and enhancing damage tolerance. The effects of various parameters on the impact response are critically analyzed. The findings presented herein contribute to the development of lightweight, impact-resistant FML components for aerospace, automotive, and defence applications.

Unraveling the relationship between stress exposure and childhood anxiety: Considering accumulation, impact, and type in the first five years of life.

Exposure to stressful events is linked to anxiety symptoms in children, although research examining this association in the first five years of life is limited. We sought to examine the role of various aspects of family stressful experiences such as the total accumulation, impact, and type (measured longitudinally in the first five years of life) on child anxiety symptoms at age 5 years. A community sample of children and their parents (N = 399) enrolled in a longitudinal study of emotion processing were assessed when the children were infants and at ages 2 years, 3 years, and 5 years. Parents completed the Revised Life Events Questionnaire (all visits) to assess family exposures to stressful life events and the Child Behavior Checklist (5 years) to assess child emotional and behavioral symptoms. Analyses showed that total stressful events accumulated by 4 years were significantly associated with child anxiety symptoms at 5 years (r = 0.118, p = 0.045). Total stressful events accumulated at earlier time points (by 1 year, 2 years, and 3 years of age) were not significantly associated with child anxiety symptoms at 5 years. Events reported as being impactful by families appeared to be more sensitive than total events, with accumulated impactful events as early as 1 year being significantly associated with child anxiety symptoms at 5 years (r = 0.112, p = 0.042). When considering types of stressors, cumulative exposure from the prenatal period to 5 years to financial stressors (β = 0.12, p = .035) was most saliently and significantly associated with child anxiety symptoms at 5 years (after adjusting for other categories of stress such as health, interpersonal, and logistical stressors). Together, these findings suggest that stressful life events accumulated in early life, particularly those rated as impactful for the family and those related to finances, are associated with child anxiety symptoms at 5 years.

LINEAR DENSITY CHRONICLES: INVESTIGATING THE IMPACT OF E-GLASS THERMOSET AND THERMOPLASTIC COMPOSITES

This comprehensive investigation delves into the mechanical characteristics of E-glass reinforcement at varying linear densities in two-dimensional (2D) woven fabric-reinforced composites employing both thermoplastic and thermoset matrices. By scrutinizing tensile strength, flexural strength, edge-wise impact resistance and out-of-plane impact properties, the study optimizes composite materials and sheds light on the influence of linear density on the mechanical properties of thermoset and thermoplastic composites. Key insights underscore the superior in-plane load-bearing capacity of thermoset composites under quasi-static conditions, contrasting with the exceptional edge-wise and out-of-plane impact resistance exhibited by thermoplastic composites. Furthermore, the study reveals that thermoset composites outperform their thermoplastic counterparts in tensile and flexural properties, with discernible deviations in quasi-static mechanical properties with increasing linear density. In both thermoplastic and thermoset composites, specimens that had lower linear density reinforcement demonstrated enhanced mechanical performance under quasi-static circumstances. Nevertheless, when subjected to dynamic conditions, thermoplastic composites exhibited this pattern, whereas thermoset composites demonstrated divergent characteristics. In the context of low-velocity impact events, it was shown that Thermoplastic 600 Tex Glass Fabric Reinforced Composite (TP6G2DFRC) exhibited greater performance compared to all other specimens, even those with higher linear density. Conversely, in thermoset composites, Thermoplastic 1200 Tex Glass Fabric Reinforced Composite (TS12G2DFRC) demonstrated notable superiority over Thermoplastic 600 Tex Glass Fabric Reinforced Composite (TS6G2DFRC), despite possessing a higher linear density

Integrating Knowledge Graphs and Digital Twins for Heritage Building Conservation

This study presents a framework for integrating digital twins and knowledge graphs to enhance heritage building conservation, addressing challenges in environmental stress management, material degradation, and structural integrity while preserving historical authenticity. Using validated synthetic data, the framework enables real-time monitoring, predictive maintenance, and emergency response through a digital twin connected to a knowledge graph. Four scenarios were simulated to evaluate system performance: high humidity exceeding a 75% threshold triggered alerts for limestone maintenance; temperature fluctuations caused strain levels up to 0.009 units in load-bearing components at 35 °C, necessitating structural inspection; cumulative degradation monitoring projected re-plastering needs by month eight as the plaster degradation index approached 85%; and sudden impact events simulated emergency responses, with strain spikes over 0.004 units prompting real-time alerts within 2.5 s. Response times averaged 50 ms under normal conditions, peaking at 150 ms during high-frequency updates, showing robust Application Programming Interface (API) performance and data synchronization. SPARQL (SPARQL Protocol and RDF Query Language) queries within the knowledge graph facilitated proactive maintenance scheduling, reducing reactive interventions and supporting sustainable heritage conservation, especially suited to humid–temperate climates. This framework offers a novel, structured approach that bridges modern technology with heritage preservation needs, addressing both urgent conservation challenges and long-term sustainability to ensure the resilience of heritage buildings.

Critical Role of Area Weighting on Estimated Long-Term Global Warming and Heat Wave Trends

Regular longitude-latitude grids are commonly used in reanalysis and climate prediction model datasets. However, this approach can disproportionately represent high-latitude regions if simple averaging is applied, leading to overestimation of their contribution. To explore the impact of Earth’s curvature on global warming and heat wave frequency, this study analyzed 450 years of surface temperature data (1850–2300) from a climate prediction model. When area weighting was applied, the global average temperature for the 1850–2300 period was found to be 8.2 °C warmer than in the unweighted case, due to the reduced influence of colder temperatures in high latitudes. Conversely, the global warming trend for the weighted case was 0.276 °C per decade, compared to 0.330 °C per decade for the unweighted case, reflecting a moderation of the polar amplification trend. While unweighted models projected a 317% increase in the frequency of global heat waves above 35 °C by 2300 compared to 1850, the weighted models suggested this frequency might be overestimated by up to 5.4%, particularly due to reduced weighting for subtropical deserts relative to tropical regions. These findings underscore the importance of accounting for Earth’s curvature in climate models to enhance the accuracy of climate change projections.

Role of risk perception on trust, event impact experiences, and event support in the Tokyo 2020 Olympics during the COVID-19 outbreak

ABSTRACT The study of the risks associated with large-scale events has become increasingly prominent in recent decades. Recently, COVID-19 has emerged as an unexpected and unprecedented risk factor for hosting the Tokyo 2020 Olympics. Risk perception is widely acknowledged as a significant factor in social exchange situations where individuals rely on one another. Trust plays a crucial role in simplifying complex scenarios and facilitating the acceptance of potential risks. However, few studies have investigated the association between trust/risk perception and event experiences/event support. This study examined the effect of Tokyo residents’ trust in event organizers and risk perception in the context of COVID-19 on their event impact experiences and event support during the Tokyo 2020 Olympics through panel data analysis (n = 938). Trust and risk were measured pre-event (T1; one month before), and event impact experiences and event support were estimated post-event (T2; two weeks after). The results indicated that trust had a positive relationship with positive event impact experiences/event support, and a negative relationship with negative event impact experiences and risk perception. Risk perception was positively associated with negative event impact experiences, and mediated the relationship between trust and negative event impact experiences. Furthermore, positive and negative event impact experiences mediated the association between trust and event support. Thus, we advanced social exchange theory by demonstrating that trust is a valuable predictor of increasing/decreasing the benefits/costs of events to residents, while confirming that risk perception increases the costs of the exchange process between the event organizer and residents in mega-sporting events.