Property Damage Research Articles

Peridynamic simulation of micro-internal damage and macro-mechanical properties of cement paste under freeze-thaw cycles

Concrete is a multi-phase, multi-component, heterogeneous composite material and a typical porous medium with a complex microstructure. Under freeze-thaw cycles (FTCs), pressures from the freezing and migration of water within the pores of the concrete cause freeze-thaw damage, which is influenced by its microstructure. To effectively simulate and understand the freeze-thaw damage mechanism, it is crucial to use a geometric structure that reflects the actual pore distribution. This study employs the μic model to create a geometric structure of hardened cement paste, capturing the spatial distribution of pores. The study utilizes the peridynamics (PD) method based on ice formation and crystallization thermodynamics to investigate the freeze-thaw damage evolution and the deterioration of mechanical properties in cement paste under FTCs. The simulations revealed the entire freeze-thaw damage process of cement paste, including internal crack initiation and propagation, as well as surface freeze-thaw spalling. The freeze-thaw damage of cement paste intensifies with increasing FTCs, and the extension of cracks diminishes the interconnection among the skeletons constituting the cement paste microstructure, leading to decreased tensile strength and elastic modulus. Additionally, the stress-strain evolution behavior of freeze-thaw cement paste under uniaxial tensile loads was obtained. The tensile strength and elastic modulus of cement paste decrease with increasing FTCs, and the higher the water-cement (w/c) ratio, the greater the reduction in strength and modulus.

Road traffic accidents and the contributing factors among drivers of public transportation in Mizan Aman town, Ethiopia: a Community-Based Cross-Sectional Study.

Traffic accidents on the road is an accident is a terrible accident that causes death, injury, and property damage. However, limited studies were addressed to investigate the prevalence of traffic accidents on the road and the contributing factors among drivers that help in developing strategies to cop-up the incidence within the research domain in Ethiopia, particularly in the study area. This study aimed to assess the prevalence of road traffic accidents and the contributing factors among drivers of public transportation in Mizan Aman town, Ethiopia. A community-based cross-sectional survey was employed among 376 drivers of public transportation. Every research subject was selected by using a simple random sampling technique. Semi-structured and open-ended questionnaires which comprised demographic characteristics, risky personal behaviors and lifestyles, driver's factors, vehicle condition, and environmental conditions were used to gather data. And then after, data was collected through interviewer-administered using KoBo Collect tools. Completed data were edited and cleaned in the Kobo collect toolbox and then exported for additional analysis to a statistical tool for social science statistics version 26. The descriptive statistics were displayed as figures, tables, and texts. Binary logistic regression was analyzed to identify the contributing factors. Statistically significant was decided with a p-value of ≤ 0.05. The results showed that the prevalence of road traffic accidents among drivers of public transportation in Mizan Aman town was 17%. The study identified factors influencing traffic accidents on the roads including marital status (being single), employee condition (permanent), monthly income (1001-2500 Ethiopia Birr), alcohol use, vehicle maintenance (not), road type (non-asphalt), and weather conditions (being windy). The overall prevalence of road traffic accidents among drivers of public transportation in Mizan Aman town was relatively low. Despite this, sociodemographic characteristics, driver factors, vehicle conditions, and environmental conditions [road type and weather conditions] were the predicting factors of traffic accidents in town. Therefore, reduction strategies should be the highest priority duty for concerned bodies like Mizan Aman town road and transport office, Bench Sheko zone transport and logistics office, and Southwest Ethiopia People Regional State (SWEPRS) transport bureau in the study area.

AFFIRMING THE LINK BETWEEN WORKERS’ SAFETY BEHAVIOR AND ORGANISATIONAL SAFETY PERFORMANCE OF SME MANUFACTURING

In the Malaysian context, small and medium enterprises (SMEs) encountered workplace accidents. Scholarly consensus attributes a substantial portion of these accidents and injuries to the human factor, specifically unsafe behaviours. This study, conducted in Malaysia’s northern region, focused on Safety and Health/Human Resource professionals within the manufacturing sector of SMEs. We compiled a robust dataset comprising 107 responses garnered through a meticulously designed self-administered questionnaire. To scrutinise the intricate relationship between safety behaviour and safety performance, we subjected the data to rigorous analysis using SmartPLS 3.2.9, employing advanced partial least square–structural equation modelling (PLS-SEM) techniques. The research outcomes unequivocally underscore the palpable and consequential impact of safety behaviour variables, namely safety compliance and safety participation, on improving safety performance, gauged through parameters such as accidents, injuries, and property damages. These results resoundingly validate the research hypotheses. Consequently, this study accentuates the importance of cultivating employee safety behaviour, particularly in resource-constrained SME settings, as an imperative step toward enhancing workplace safety.

DCP-Net: An Efficient Image Segmentation Model for Forest Wildfires

Wildfires usually lead to a large amount of property damage and threaten life safety. Image recognition for fire detection is now an important tool for intelligent fire protection, and the advancement of deep learning technologies has enabled an increasing number of cameras to possess functionalities for fire detection and automatic alarm triggering. To address the inaccuracies in extracting texture and positional information during intelligent fire recognition, we have developed a novel network called DCP-Net based on UNet, which excels at capturing flame features across multiple scales. We conducted experiments using the Corsican Fire Dataset produced by the “Environmental Science UMR CNRS 6134 SPE” laboratory at the University of Corsica and the BoWFire Dataset by Chino et al. Our algorithm was compared with networks such as SegNet, UNet, UNet++, and PSPNet, demonstrating superior performance across three metrics: mIoU, F1-score, and OA. Our proposed deep learning model achieves the best mIoU (78.9%), F1-score (76.1%), and OA (96.7%). These results underscore the robustness of our algorithm, which accurately identifies complex flames, thereby making a significant contribution to intelligent fire recognition. Therefore, the proposed DCP-Net model offers a viable solution to the challenges of wildfire monitoring using cameras, with hardware and software requirements typical of deep learning setups.

DESIGN AND FABRICATION OF EMERGENCY FIRE RESCUE MACHINE

Building fires pose a serious risk to people, property and the environment. Numerous factors, such as human mistakes, electrical issues, heating systems, combustible items, might cause fire accidents. The effects of fire can be disastrous, resulting in accidents, fatalities, and significant building damage. Compared to fires in low-rise structures, high-rise building fire incidents pose numerous difficulties and a greater risk. High-rise building's vertical structures, greater resident capacity, and intricate infrastructure make fire fighters and evacuating residents more difficult to escape. It is essential to comprehend the causes, effects, and preventive strategies unique to high-rise fires in order to ensure occupant safety and reduce property damage. The disturbingly high rate of fire events, which are frequently brought on by inadequate fire prevention measures and damaged infrastructure, represent a serious threat to people's lives, their property, and the health of the economy. India can reduce the dangers connected with fire incidents in high-rise structures and guarantee the safety and well-being of its residents in the urban environment by giving priorityto the implementation of appropriate rescue systems. So in this project we are going to develop such machine which can help humans to get escape in the case of emergency situation in high rise building.

Study on the mechanical and damage properties of laminated limestone under acid mine drainage dissolution

To explore the chemical and mechanical effects of acid mine drainage on water and rock, acid mine drainage (AMD) dissolution tests, triaxial compression tests, and acoustic emission tests were performed on limestone rock samples with different bedding dip angles. Combined with scanning electron microscopy and nuclear magnetic resonance analyses, the changes in the internal pores and surface morphologies of the rock samples before and after dissolution were analyzed. The results were as follows. (1) AMD dissolution mainly occurred in the shallow surfaces and bedding planes of the limestone samples. During dissolution, the shape of the matrix crystal disappeared to form small pores, and residual substances appeared during the dissolution of the bedding plane. These small pores were prone to the creation of large honeycomb-like dissolved pores. (2) With increasing bedding plane angle, the compressive strengths and elastic moduli of the limestone samples exhibited V-shaped distributions. Additional branch cracks were derived from the limestone samples after dissolution, and dissolution reduced the mechanical strength of the limestone by decreasing the crack initiation stress and damage stress. (3) With increasing bedding dip angle, the uniaxial failure modes of the rock samples changed from matrix tensile failure and shear failure along the bedding plane to plane tensile failure. After dissolution, the limestone matrix was prone to cracking and spalling along the surface of the sample. (4) There were differences in the triaxial compression failure modes between the dissolved limestone and the undissolved limestone. When α = 0° or 90°, the limestone samples formed additional branch fissures after dissolution. When α = 45°, the formation of penetrating cracks along the bedding plane was obviously controlled by the bedding plane. (5) A chemical–mechanical damage model was established and modified by the compression coefficient K, which could effectively reflect the deformation of the dissolved rock sample during loading.

A Novel Fracturing Fluid Based on Functionally Modified Nano-Silica-Enhanced Hydroxypropyl Guar Gel.

Considering the damage caused by conventional fracturing fluid in low-permeability reservoirs, a novel fracturing fluid (FNG) combining hydroxypropyl guar (HPG) and functionally modified nano-silica (FMNS) was prepared. The properties of heat/shear resistance, rheological property, proppant transportation, and formation damage were evaluated with systematic experiments. The results showed that the viscosities of FNG before and after the heat/resistance were 1323 mPa·s and 463 mPa·s, respectively, while that of conventional HPG gel was 350 mPa·s. FNG is a pseudoplastic strong gel with a yield stress of 12.9 Pa, a flow behavior index of 0.54, an elastic modulus of 16.2 Pa, and a viscous modulus of 6.2 Pa. As the proportions of proppant mass in further sections transported with FNG were higher than those transported with HPG gel, FNG could transport the proppant better than HPG gel at high temperatures. Because of the amphiphilic characteristics of FMNS, the surface/interface properties were improved by the FNG filtrate, resulting in a lower oil permeability loss rate of 10 percentage points in the matrix than with the filtrated HPG gel. Due to the considerable residual gel in broken HPG gel, the retained conductivity damaged with broken FNG was 9.5 percentage points higher than that damaged with broken HPG gel. FNG shows good potential for reducing formation damage during fracturing in low-permeability reservoirs in China.

Developing CBTC system safety requirement hierarchy through STPA methodology

Safety-critical systems, such as the railway signal system, are subject to potentially high costs from failures, including loss of life and property damage. The use of new technology, including communication-based train control (CBTC) systems with software and computers, has changed the types of accidents that occur. Software-related issues and dysfunctional interactions between system components controlled by the software are increasingly the cause of incidents. Developing a “safe” safety-critical system requires accurate and complete safety requirements, which are the foundation of system development. Traditional hazard analysis techniques are insufficient for identifying the causes of accidents in modern railway signaling systems. Systems-Theoretic Process Analysis (STPA) is a powerful new hazard analysis method designed to address these limitations. Building upon this foundation, a hierarchical approach to safety requirement development has been further developed. This approach combines STPA analysis with a hierarchical modeling approach to establish traceability links from safety requirements to specific architectures, refine and allocate system-level safety requirements to relevant subsystems, and abstract safety requirements at higher hierarchical levels to enable easy changes to lower-level implementations. This paper employs the aforementioned methodology within the context of the CBTC system, thereby enhancing risk management and hazard analysis, enabling early insights, and facilitating the generation of safety requirements of CBTC System.

Drink…then drive away: The effects of lowering the blood alcohol concentration in Utah.

In March of 2017 Utah announced its intent to lower the legal blood alcohol content (BAC) for driving from 0.08 to 0.05g/dL. However, this change did not take effect until 2019. We employ a difference-in- differences strategy on Utah counties using neighboring states as controls to test whether this policy change significantly affected the number of traffic accidents or the severity of those accidents. Results show the policy appears to temporarily decrease the total number of accidents, limited primarily to property damage- only accidents. We believe these results may be partially explained by drivers who, after the policy is enacted, avoid reporting property damage-only accidents if possible. Using insurance claims data, we show there is no corresponding fall in insurance claims or payouts suggesting that the fall in total accidents likely comes from under-reporting.

Research on Drivers' Behaviors of Heavy Goods Vehicles Based on Traffic Accident Analysis

Heavy goods vehicles (HGVs) have played a vital role in transporting goods. These goods are closely related to social development and people's lives, including daily necessities and parcels, and even industrial raw materials such as coal and steel. However, for many reasons, the number and incidence of HGVs traffic accidents remain high in all countries. Since HGVs have the characteristics of large volume, large mass and fast speed, the consequences of HGVs traffic accidents are often very serious, causing numerous property damages, and even seriously threatening people's lives. There are many factors that contribute to this tragedy, but it is worth noticing that HGVs drivers played a vital role. Their bad driving behaviors and habits may be to blame for the accidents. This paper identifies the bad driving behaviors and habits of HGVs drivers that may lead to traffic accidents, and analyzes the impact of these behaviors. This paper also attempts to establish the relationship between external factors such as insurance and drivers' driving behaviors, and explores the impact of public policies on traffic accidents. Finally, this paper draws the conclusion that many bad driving behaviors will lead to HGVs accidents, and several actions from drivers, government and companies can be taken to improve traffic safety. This paper adopts the field observation method, the interview survey method and the literature research method.

Stacking Ensemble Learning with Regression Models for Predicting Damage from Terrorist Attacks

Terrorist attacks can cause unexpectedly enormous damage to lives and property. To prevent and mitigate damage from terrorist activities, governments and related organizations must have suitable measures and efficient tools to cope with terrorist attacks. This work proposed a new method based on stacking ensemble learning and regression for predicting damage from terrorist attacks. First, two-layer stacking classifiers were developed and used to indicate if a terrorist attack can cause deaths, injuries, and property damage. For fatal and injury attacks, regression models were utilized to forecast the number of deaths and injuries, respectively. Consequently, the proposed method can efficiently classify casualty terrorist attacks with an average area under precision-recall curves (AUPR) of 0.958. Furthermore, the stacking model can predict property damage attacks with an average AUPR of 0.910. In comparison with existing methods, the proposed method precisely estimates the number of fatalities and injuries with the lowest mean absolute errors of 1.22 and 2.32 for fatal and injury attacks, respectively. According to the superior performance shown, the stacking ensemble models with regression can be utilized as an efficient tool to support emergency prevention and management of terrorist attacks.

Three-Dimensional ERT Advanced Detection Method with Source-Position Electrode Excitation for Tunnel-Boring Machines

Tunnel-boring machines (TBMs) are widely used in urban underground tunnel construction due to their fast and efficient features. However, shield-tunnel construction faces increasingly complex geological environments and may encounter geological hazards such as faults, fracture zones, water surges, and collapses, which can cause significant property damage and casualties. Existing geophysical methods are subject to many limitations in the shield-tunnel environment, where the detection space is extremely small, and a variety of advanced detection methods are unable to meet the required detection requirements. Therefore, it is crucial to accurately detect the geological conditions in front of the tunnel face in real time during the tunnel boring process of TBM tunnels. In this paper, a 3D-ERT advanced detection method using source-position electrode excitation is proposed. First, a source-position electrode array integrated into the TBM cutterhead is designed for the shield-tunnel construction environment, which provides data security for the inverse imaging of the anomalous bodies. Secondly, a 3D finite element tunnel model containing high- and low-resistance anomalous bodies is established, and the GREIT reconstruction algorithm is utilized to reconstruct 3D images of the anomalous body in front of the tunnel face. Finally, a physical simulation experiment platform is built, and the effectiveness of the method is verified by laboratory physical modeling experiments with two different anomalous bodies. The results show that the position and shape of the anomalous body in front of the tunnel face can be well reconstructed, and the method provides a new idea for the continuous detection of shield construction tunnels with boring.

Localized heat treatment and XFEM-based investigation of damage mechanisms in reinforced notched plates under uniaxial tensile stress

This study delves into mechanical engineering by investigating damage mechanisms and reinforcement strategies in notched structures. Our research centers on enhancing structural integrity through a localized heat treatment technique applied at the notch before uniaxial tensile stress testing. Central to our approach is Functionally Graded Material (FGM), defined by volume fractions that specific exponents modulate. The innovation lies in treating the interface between the base metal and the heat-affected zone (HAZ). We introduce a novel meshing-based grading technique that facilitates elastoplastic behavior and allows for the gradation of damage properties. This technique is particularly noteworthy as it obviates the need for complex subroutines or FORTRAN compilation. Our methodology employs a mesh model with independent elements, each characterized by a volume fraction gradient to accurately represent the graded material’s behavior. The behavioral model is anchored in the Von Mises equivalent stress flow theory, and tensile tests inform the material properties of steel and the HAZ. The results of our investigation are presented through force-displacement curves, which critically evaluate our reinforcement technique. Moreover, using the eXtended Finite Element Method (XFEM) in our meshing approach has proven particularly effective in delineating structural behavior post-fissure initiation, showcasing its superiority in capturing the nuances of crack propagation and structural separation.

The Machine Learning Attribution of Quasi-Decadal Precipitation and Temperature Extremes in Southeastern Australia during the 1971–2022 Period

Much of eastern and southeastern Australia (SEAUS) suffered from historic flooding, heat waves, and drought during the quasi-decadal 2010–2022 period, similar to that experienced globally. During the double La Niña of the 2010–2012 period, SEAUS experienced record rainfall totals. Then, severe drought, heat waves, and associated bushfires from 2013 to 2019 affected most of SEAUS, briefly punctuated by record rainfall over parts of inland SEAUS in the late winter/spring of 2016, which was linked to a strong negative Indian Ocean Dipole. Finally, from 2020 to 2022 a rare triple La Niña generated widespread extreme rainfall and flooding in SEAUS, resulting in massive property and environmental damage. To identify the key drivers of the 2010–2022 period’s precipitation and temperature extremes due to accelerated global warming (GW), since the early 1990s, machine learning attribution has been applied to data at eight sites that are representative of SEAUS. Machine learning attribution detection was applied to the 52-year period of 1971–2022 and to the successive 26-year sub-periods of 1971–1996 and 1997–2022. The attributes for the 1997–2022 period, which includes the quasi-decadal period of 2010–2022, revealed key contributors to the extremes of the 2010–2022 period. Finally, some drivers of extreme precipitation and temperature events are linked to significant changes in both global and local tropospheric circulation.

Hierarchical Predictions of Fine-to-Coarse Time Span and Atmospheric Field Reconstruction for Typhoon Track Prediction

The prediction of typhoon tracks in the Northwest Pacific is key to reducing human casualties and property damage. Traditional numerical forecasting models often require substantial computational resources, are high-cost, and have significant limitations in prediction speed. This research is dedicated to using deep learning methods to address the shortcomings of traditional methods. Our method (AFR-SimVP) is based on a large-kernel convolutional spatio-temporal prediction network combined with multi-feature fusion for forecasting typhoon tracks in the Northwest Pacific. In order to more effectively suppress the effect of noise in the dataset to enhance the generalization ability of the model, we use a multi-branch structure, incorporate an atmospheric reconstruction subtask, and propose a second-order smoothing loss to further improve the prediction ability of the model. More importantly, we innovatively propose a multi-time-step typhoon prediction network (HTAFR-SimVP) that does not use the traditional recurrent neural network family of models at all. Instead, through fine-to-coarse hierarchical temporal feature extraction and dynamic self-distillation, multi-time-step prediction is achieved using only a single regression network. In addition, combined with atmospheric field reconstruction, the network achieves integrated prediction for multiple tasks, which greatly enhances the model’s range of applications. Experiments show that our proposed network achieves optimal performance in the 24 h typhoon track prediction task. Our regression network outperforms previous recurrent network-based typhoon prediction models in the multi-time-step prediction task and also performs well in multiple integration tasks.

Seismic Fragility Analysis of Existing Old Newari Brick Masonry Building in Pokhara Valley

Most of the building stock in Nepal is based on masonry construction, which includes monumental, administrative, and residential structures. These structures are vulnerable during earthquakes, as evidenced by the massive structural damage, loss of human life, and property damage due to a lack of proper assessment and appropriate strengthening measures. An analysis of the seismic vulnerability of existing old Newari brick masonry buildings in the Pokhara Valley is presented. These buildings were built using indigenous knowledge and technology. The investigation is based on analytical studies, with some material properties obtained from field tests. Proper modeling of a masonry structure is crucial for reliable seismic resistance and structural design. However, modeling a real masonry structure is a challenging and computationally demanding task due to its complicated framework, requiring in-depth knowledge, realistic material properties, and relevant information. The aim of this research is to assess the seismic performance of old Newari masonry buildings using stress level and fragility curves. The research issues are addressed analytically through linear time history analysis using the finite element program-based software Sap 2000 v20. In dynamic analysis, numerical building models were subjected to three synthetic earthquakes. The performance status of the building based on various stress levels is evaluated, and weak regions are identified. The fragility curve of the structure is assessed, considering the ground motion parameters in the locality. The fragility function is plotted with the probability of failure at an interval of 0.10 g. The results of the analysis highlight that the studied structure is vulnerable compared to the codal provisions and standard recommendations.

An Improved Fire Detection Approach Based On Yolo-v8 for Smart Cities

Systems for detecting fires are essential for preventing property damage and saving lives. defending people and property. Conventional techniques frequently depend on sensor-based strategies, which have limitations in intricate settings. In order to improve accuracy and efficiency, this study suggests an intelligent fire detection system that makes use of machine learning and computer vision techniques. The technology analyzes video streams in real time using deep learning algorithms to identify fire incidents based on visual patterns and attributes. Future research on fire detection systems will benefit from the information this study will provide for smoker and fire detection issues in both indoor and outdoor situations. The improved fire detection technique for smart cities that is based on the YOLOv8 algorithm is the smart fire detection system (SFDS), which uses deep learning to identify fire-specific properties in real-time. The SFDS strategy may be more cost-effective, reduce false alarms, and improve fire detection accuracy when compared to traditional methods. It can also be extended to find other intriguing aspects of smart cities, such as gas leakage or flooding. The proposed smart city framework consists of four primary levels: the application layer (i), cloud layer (iii), fog layer (ii), and internet of things layer (iv). The recommended technique uses fog, cloud computing, and the Internet of Things layer to collect and understand data in real time. This reduces the chance of damage to persons or property and enables faster reaction times. The SFDS demonstrated state- of-the-art performance in terms of precision and recall, with a high precision rate of 97.1% across all classes. Among the potential applications are intelligent security systems, forest fire monitoring, and public space fire safety management.

Possibility of Suppressing Fire Spread in the Event of a Large Earthquake by Greening and Demolishing Vacant Houses

In recent years, disaster mitigation measures by greening and demolition of vacant houses to prevent large post-earthquake fire spread have been promoted. However, few studies evaluate its effects on reducing property damage and human casualties, especially in vulnerable areas for earthquakes, such as densely built-up wooden residential areas. In this paper, we develop a model to describe the delay of post-earthquake fire spread between neighboring buildings, and incorporate it into the simulation model. Using the integrated model, we evaluate the reduction of the number of burnt-down buildings and human casualties in densely built-up wooden residential areas, and demonstrate the effectiveness of greening and demolition of vacant houses.

Effect of Moisture Content and Wet–Dry Cycles on the Strength Properties of Unsaturated Clayey Sand

Based on the actual situation of the project on the Weihai–Yanhai Expressway section of Rongwu Expressway, the effects of water content change and the dry–wet cycle on the mechanical behavior of unsaturated clayey sandy soil were analyzed in this study. In this study, ventilated undrained triaxial shear tests were carried out on unsaturated clayey sandy soils with different water contents (6%, 8%, 10%, 12%, 14% and 16%). Concurrently, the soil samples were subjected to three distinct wet and dry cycle pathways (2~22%, 2~12%, and 12~22%) to gain an understanding of how the mechanical features of the soil changed under the different conditions. The test findings demonstrate that when the water content increases, the unsaturated clayey sandy soil’s cohesiveness and shear strength diminish. The strength of shear decline exhibits a pattern of first being quick, followed by sluggish. The strength of shear and cohesiveness of clayey sandy soil declined under the influence of the dry and wet cycles, with the first cycle primarily affecting variations in cohesiveness and strength of shear. Furthermore, the strength of shear and cohesiveness of clayey sandy soil diminish more with increasing wet and dry cycle amplitude and upper water content limits. Lastly, the drying shrinkage and hygroscopic expansion of clay particles in clayey sandy soils during wet and dry cycles are not significant, resulting in less structural damage and deterioration of the mechanical properties of the soils. The study’s findings have a significant impact on the durability of roadbeds made of unsaturated clayey sandy soil in both wet and dry situations.

Contextualizing Representations of Violence in #Blacklivesmatter Coverage with Inductive Computational Content Analysis

ABSTRACT News coverage of social movements traditionally adheres to the protest paradigm, which focuses on violent and confrontational tactics instigated by activists. Recent work has called for a reevaluation of this theory through the lens of partisan media bias. We demonstrate how partisan media bias systematically shapes coverage of the summer 2020 Black Lives Matter protests that occurred in the aftermath of the police murder of George Floyd. Using an inductive computational content analysis, we surface words and phrases that best predict whether an article was published in a left-, center-, or right-leaning outlet. We then perform a qualitative close reading of those terms in context to determine how partisan media bias shapes depictions of the same events: namely, in representations of protestors and violence. Right-leaning media depict protestors as violent rioters, looters, and antifa members committing property damage, whereas center- and left-leaning media emphasize the peaceful nature of the protests motivated by the police killing of Floyd and systematic racism. We consider the implications of these findings on broader public opinion within media ecosystems.