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As the climate phenomenon sends warm water surging across the eastern Pacific, some parts of the fishing industry are suffering—but other regions are seeing a windfall
Global catastrophic risk events, such as nuclear war, pose a severe threat to the stability of international financial systems. As evidenced by even less severe scenarios like the Great Recession, an economic failure can propagate through the world trade network, wreaking havoc on the global economy. While the contemporary literature on cascading failure models addresses this issue qualitatively, a simple and intuitive quantitative estimation that could be used in integrated assessment frameworks is missing. In this study, we introduce a quantitative network model of global financial cascading failure. Our proposal is a fast, efficient, single free parameter model, following a straightforward logic of propagating failures. We fit the model to the Great Recession and test it against historical examples and commercial analysis. We also provide predictions for a hypothetical armed conflict between India and Pakistan. Our aim is to introduce a quantitative approach that could inform policy decisions by contextualising global catastrophic scenarios regarding financial losses and assessing the effectiveness of resilience strategies, complementing existing models and frameworks for broade
In many systems, servers do not turn on instantly; instead, a setup time must pass before a server can begin work. These "setup times" can wreak havoc on a system's queueing; this is especially true in modern systems, where servers are regularly turned on and off as a way to reduce operating costs (energy, labor, CO2, etc.). To design modern systems which are both efficient and performant, we need to understand how setup times affect queues. Unfortunately, despite successes in understanding setup in a single-server system, setup in a multiserver system remains poorly understood. To circumvent the main difficulty in analyzing multiserver setup, all existing results assume that setup times are memoryless, i.e. distributed Exponentially. However, in most practical settings, setup times are close to Deterministic, and the widely used Exponential-setup assumption leads to unrealistic model behavior and a dramatic underestimation of the true harm caused by setup times. This paper provides a comprehensive characterization of the average waiting time in a multiserver system with Deterministic setup times, the M/M/k/Setup-Deterministic. In particular, we derive upper and lower bounds on the
Agriculture faces a growing challenge with wildlife wreaking havoc on crops, threatening sustainability. The project employs advanced object detection, the system utilizes the Mobile Net SSD model for real-time animal classification. The methodology initiates with the creation of a dataset, where each animal is represented by annotated images. The SSD Mobile Net architecture facilitates the use of a model for image classification and object detection. The model undergoes fine-tuning and optimization during training, enhancing accuracy for precise animal classification. Real-time detection is achieved through a webcam and the OpenCV library, enabling prompt identification and categorization of approaching animals. By seamlessly integrating intelligent scarecrow technology with object detection, this system offers a robust solution to field protection, minimizing crop damage and promoting precision farming. It represents a valuable contribution to agricultural sustainability, addressing the challenge of wildlife interference with crops. The implementation of the Intelligent Scarecrow Monitoring System stands as a progressive tool for proactive field management and protection, empower
Ransomware attacks continue to wreak havoc across the globe, with public reports of total ransomware payments topping billions of dollars annually. While the use of cryptocurrency presents an avenue to understand the tactics of ransomware actors, to date published research has been constrained by relatively limited public datasets of ransomware payments. We present novel techniques to identify ransomware payments with low false positives, classifying nearly \$700 million in previously-unreported ransomware payments. We publish the largest public dataset of over \$900 million in ransomware payments -- several times larger than any existing public dataset. We then leverage this expanded dataset to present an analysis focused on understanding the activities of ransomware groups over time. This provides unique insights into ransomware behavior and a corpus for future study of ransomware cybercriminal activity.
We demonstrate that shadows cast on a proto-planetary disk can drive it eccentric. Stellar irradiation dominates heating across much of these disks, so an uneven illumination can have interesting dynamical effects. Here, we focus on transition disks. We carry out 3D Athena++ simulations, using a constant thermal relaxation time to describe the disk's response to changing stellar illumination. We find that an asymmetric shadow, a feature commonly observed in real disks, perturbs the radial pressure gradient and distorts the fluid streamlines into a set of twisted ellipses. Interactions between these streamlines have a range of consequences. For a narrow ring, an asymmetric shadow can sharply truncate its inner edge, possibly explaining the steep density drop-offs observed in some disks and obviating the need for massive perturbers. For a wide ring, such a shadow can dismantle it into two (or possibly more) eccentric rings. These rings continuously exert torque on each other and drive gas accretion at a healthy rate, even in the absence of disk viscosity. Signatures of such twisted eccentric rings may have already been observed as, e.g., twisted velocity maps inside gas cavities. We
Few real-world systems are amenable to truly Bayesian filtering; nonlinearities and non-Gaussian noises can wreak havoc on filters that rely on linearization and Gaussian uncertainty approximations. This article presents the Bayesian Recursive Update Filter (BRUF), a Kalman filter that uses a recursive approach to incorporate information from nonlinear measurements. The BRUF relaxes the measurement linearity assumption of the Extended Kalman Filter (EKF) by dividing the measurement update into a user-defined number of steps. The proposed technique is extended for ensemble filters in the Bayesian Recursive Update Ensemble Kalman Filter (BRUEnKF). The performance of both filters is demonstrated in numerical examples, and new filters are introduced which exploit the theoretical foundation of the BRUF in different ways. A comparison between the BRUEnKF and Gromov flow, a popular particle flow algorithm, is presented in detail. Finally, the BRUEnKF is shown to outperform the EnKF for a very high-dimensional system.
Extreme precipitation wreaks havoc throughout the world, causing billions of dollars in damage and uprooting communities, ecosystems, and economies. Accurate extreme precipitation prediction allows more time for preparation and disaster risk management for such extreme events. In this paper, we focus on short-term extreme precipitation forecasting (up to a 12-hour ahead-of-time prediction) from a sequence of sea level pressure and zonal wind anomalies. Although existing machine learning approaches have shown promising results, the associated model and climate uncertainties may reduce their reliability. To address this issue, we propose a self-attention augmented convolution mechanism for extreme precipitation forecasting, systematically combining attention scores with traditional convolutions to enrich feature data and reduce the expected errors of the results. The proposed network architecture is further fused with a highway neural network layer to gain the benefits of unimpeded information flow across several layers. Our experimental results show that the framework outperforms classical convolutional models by 12%. The proposed method increases machine learning as a tool for gain
COVID19s widespread distribution is wreaking havoc on peoples lives all over the world. This pandemic has also had a significant impact on energy consumption. Its influence can be seen in the power systems operation and the market as well. The power consumers habits and demand curves have been changed at a breakneck pace. In this work, a one year mixed integer programming (MIP) problem has been developed to compare the power consumption between 2019 and 2020 in the United States as an example regarding the COVID19 pandemic effect in order to better prepare for possible similar future events. 100 percent renewable single microgrids (SMGs) are studied using wind turbines and photovoltaics. Batteries are also employed since it is inevitable when the system uses renewables. Additionally, it is possible for the SMGs to trade power with the main grid as needed. The effect of the SMGs clustering to form the multi microgrids (MMGs) is also considered. In order to investigate the risk of the system during the COVID19 and formation of MMG, downside risk constraints are applied to the proposed model. Furthermore, a stylized short run consumers demand model is proposed, using elasticity and as
The recent outbreak of the novel coronavirus is wreaking havoc on the world and researchers are struggling to effectively combat it. One reason why the fight is difficult is due to the lack of information and knowledge. In this work, we outline our effort to contribute to shrinking this knowledge vacuum by creating covidAsk, a question answering (QA) system that combines biomedical text mining and QA techniques to provide answers to questions in real-time. Our system also leverages information retrieval (IR) approaches to provide entity-level answers that are complementary to QA models. Evaluation of covidAsk is carried out by using a manually created dataset called COVID-19 Questions which is based on information from various sources, including the CDC and the WHO. We hope our system will be able to aid researchers in their search for knowledge and information not only for COVID-19, but for future pandemics as well.
Game theory has by now found numerous applications in various fields, including economics, industry, jurisprudence, and artificial intelligence, where each player only cares about its own interest in a noncooperative or cooperative manner, but without obvious malice to other players. However, in many practical applications, such as poker, chess, evader pursuing, drug interdiction, coast guard, cyber-security, and national defense, players often have apparently adversarial stances, that is, selfish actions of each player inevitably or intentionally inflict loss or wreak havoc on other players. Along this line, this paper provides a systematic survey on three main game models widely employed in adversarial games, i.e., zero-sum normal-form and extensive-form games, Stackelberg (security) games, zero-sum differential games, from an array of perspectives, including basic knowledge of game models, (approximate) equilibrium concepts, problem classifications, research frontiers, (approximate) optimal strategy seeking techniques, prevailing algorithms, and practical applications. Finally, promising future research directions are also discussed for relevant adversarial games.
The new coronavirus (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people's everyday lives. Fever, cough, sore throat, headache, exhaustion, muscular aches, and difficulty breathing are all typical symptoms of COVID-19. A reliable detection technique is needed to identify affected individuals and care for them in the early stages of COVID-19 and reduce the virus's transmission. The most accessible method for COVID-19 identification is RT-PCR; however, due to its time commitment and false-negative results, alternative options must be sought. Indeed, compared to RT-PCR, chest CT scans and chest X-ray images provide superior results. Because of the scarcity and high cost of CT scan equipment, X-ray images are preferable for screening. In this paper, a pre-trained network, DenseNet169, was employed to extract features from X-ray images. Features were chosen by a feature selection method (ANOVA) to reduce computations and time complexity while overcoming the curse of dimensionality to improve predictive accuracy. Finally, selected features were classified by XGBoost. The ChestX-ray8 dataset, which was employed to train a
During the beginning of 2020, the Covid-19 pandemic took the world by surprise, rapidly spreading undetected between and within many countries and wreaking havoc on the global economy both through death tolls and lockdowns. Healthcare professionals treating the coronavirus patients grapple with a massive and unprecedented shortage of Facepiece Respirators (FPRs) and other personal protective equipment (PPE), which act as fundamental tools to protect the health of the medical staff treating the patients affected by the coronavirus. While many FPRs are designed to be disposable single-use devices, the development of sterilization strategies is necessary to circumvent future shortages. Here, we describe the development of a plasma-based method to sterilize PPE such as FPRs with ozone. The novel design uses a flow-through configuration where ozone directly flows through the fibers of the PPE through the maintenance of a pressure gradient. Canonical ozone-based methods place the mask into a sealed ozone-containing enclosure but lack pressurization to permeate the mask fibers. In this device, ozone is created through an atmospheric pressure Dielectric Barrier Discharge (DBD) fed with com
The hunt for ancient life on Mars just got an important test run。 Scientists confirmed that the Rosalind Franklin rover's sophisticated instrument can detect subtle differences in two stable molecules that could preserve evidence of past life for billions of years。 But the team also uncovered a surprise: organic molecules in the Murchison meteorite
Ancient asteroid impacts may have done more than reshape Earth's surface—they could have helped spark life itself。 New computer models show the collisions created enormous underground hydrothermal systems by cracking the planet's crust and allowing hot water to flow through it。 These long-lasting, life-friendly environments may have covered much of
Astronomers have released the largest gravitational wave catalog ever, revealing 161 new black hole collisions and pushing the total number of detections to 390。 Among the highlights are the clearest gravitational wave signal ever recorded, the most accurate location of a black hole merger, and growing evidence that some black holes are the product
VW's plan calls for half as many models but didn't mention closures or job cuts
Scientists have uncovered new evidence that fireworks can pollute both the air and water in ways that extend beyond the visible smoke。 The findings show that leftover debris, fine particles, and airborne chemicals may affect ecosystems and increase people's exposure to air pollution during major celebrations