Application rate errors when using self-propelled agricultural sprayers for agricultural production remain a concern. Among other factors, spray boom instability is one of the major contributors to application errors. Spray booms' width of 38m, combined with 30 kph driving speeds, varying terrain, and machine dynamics when maneuvering complex field boundaries, make controls of these booms very complex. However, there is no quantitative knowledge on the extent of boom movement to systematically develop a solution that might include boom designs and responsive boom control systems. Therefore, this study was conducted to develop an automated computer vision system to quantify the boom movement of various agricultural sprayers. A computer vision system was developed to track a target on the edge of the sprayer boom in real time. YOLO V7, V8, and V11 neural network models were trained to track the boom's movements in field operations to quantify effective displacement in the vertical and transverse directions. An inclinometer sensor was mounted on the boom to capture boom angles and validate the neural network model output. The results showed that the model could detect the target with
Bistable tape spring booms are used on spacecraft for their ability to self-deploy using stored strain energy. However, their uncontrolled deployment can induce mechanical shocks that are variable as a function of material properties and temperature, and may damage sensitive satellite components and disrupt attitude control. Because traditional Finite Element Analysis (FEA) struggles to accurately capture this highly nonlinear behavior, we solve the inverse problem to estimate these loads from dynamic response measurements. Previous data-driven approaches using Vector Fitting required time-consuming retesting for every specific load level due to the boom's load-dependent dynamic behavior. To overcome this limitation, we introduce a parametric data-driven framework where a parametric transfer-function model of a composite tape spring boom is developed using force and velocity measurements. The parametric Adaptive Antoulas-Anderson algorithm (p-AAA) is used to construct a single parametric (multivariate) transfer function capable of capturing the nonlinear response of the boom to load amplitude. To evaluate the proposed framework, the boom is excited at its base at 15 distinct load l
Financial events negatively affect emotional well-being, but large-scale studies examining their impact on online emotional expression using real-time social media data remain limited. To address this gap, we propose analyzing Reddit communities (financial and non-financial) across two case studies: a financial crash and a boom. We investigate how emotional and psycholinguistic responses differ between financial and non-financial communities, and the extent to which the type of financial event affects user behavior during the two case study periods. To examine the effect of these events on expressed language, we analyze daily sentiment, emotion, and LIWC counts using quasi-experimental methods: Difference-in-Differences (DiD) and Causal Impact analyses during a financial boom and a financial crash. Overall, we find coherent, negative shifts in emotional responses during financial crashes, but weaker, mixed responses during booms. By exploring emotional and psycholinguistic expressions during financial events, we identify future implications for understanding online users' mental health and building connected, healthy communities.
Marine oil spills damage ecosystems, contaminate coastlines, and disrupt food webs, while imposing substantial economic losses on fisheries and coastal communities. Prior work has demonstrated the feasibility of containing and cleaning individual spills using a duo of autonomous surface vehicles (ASVs) equipped with a towed boom and skimmers. However, existing algorithmic approaches primarily address isolated slicks and individual ASV duos, lacking scalable methods for coordinating large robotic fleets across multiple spills representative of realistic oil-spill incidents. In this work, we propose an integrated multi-robot framework for coordinated oil-spill confinement and cleanup using autonomous ASV duos. We formulate multi-spill response as a risk-weighted minimum-latency problem, where spill-specific risk factors and service times jointly determine cumulative environmental damage. To solve this problem, we develop a hybrid optimization approach combining mixed-integer linear programming, and a tailored warm-start heuristic, enabling near-optimal routing plans for scenarios with tens of spills within minutes on commodity hardware. For physical execution, we design and analyze t
Small satellites such as CubeSats pose demanding requirements on the weight, size, and multifunctionality of their structures due to extreme constraints on the payload mass and volume. To address this challenge, we introduce a concept of multifunctional deployable space structures for CubeSats based on ultrathin, elastically foldable, and self-deployable bistable composite structures integrated with flexible electronics. The multifunctional bistable booms can be stored in a coiled configuration and self-deploy into a long structure upon initiation by releasing the stored strain energy. The boom demonstrates the capabilities of delivering power and transmitting data from the CubeSat to the flexible devices on the boom tip. The boom also shows the ability to monitor the dynamics and vibration during and after the deployment. A payload boom has been installed in a 3U CubeSat as flight hardware for in-space testing and demonstration. This effort combines morphable ultrathin composite structures with flexible electronics.
With the arrival of ever higher throughput wide-field surveys and a multitude of multi-messenger and multi-wavelength instruments to complement them, software capable of harnessing these associated data streams is urgently required. To meet these needs, a number of community supported alert brokers have been built, currently focused on processing of Zwicky Transient Facility (ZTF; $\sim 10^5$-$10^6$ alerts per night) with an eye towards Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST; $\sim 2 \times 10^7$ alerts per night). Building upon the system that successfully ran in production for ZTF's first seven years of operation, we introduce BOOM (Burst & Outburst Observations Monitor), an analysis framework focused on real-time, joint brokering of these alert streams. BOOM harnesses the performance of a Rust-based software stack relying on a non-relational MongoDB database combined with a Valkey in-memory processing queue and a Kafka cluster for message sharing. With this system, we demonstrate feature parity with the existing ZTF system with a throughput $\sim 7 \times$ higher. We describe the workflow that enables the real-time processing as well as the results
Solar sails provide a means of propulsion using solar radiation pressure, which offers the possibility of exciting new spacecraft capabilities. However, solar sails have attitude control challenges because of the significant disturbance torques that they encounter due to imperfections in the sail and its supporting structure, as well as limited actuation capabilities. The Cable-Actuated Bio-inspired Lightweight Elastic Solar Sail (CABLESSail) concept was previously proposed to overcome these challenges by controlling the shape of the sail through cable actuation. The structural flexibility of CABLESSail introduces control challenges, which necessitate the design of a robust feedback controller for this system. The purpose of the proposed research here is to design a robust controller to ensure precise and reliable control of CABLESSail's boom. Taking into account the system dynamics and the dynamic properties of the CABLESSail concept, a passivity-based proportional-derivative (PD) controller for a single boom on the CABLESSail system is designed. To reach the nonzero desired setpoints, a feedforward input is additionally applied to the control law and a time-varying feedforward in
The globalization of education and rapid growth of online learning have made localizing educational content a critical challenge. Lecture materials are inherently multimodal, combining spoken audio with visual slides, which requires systems capable of processing multiple input modalities. To provide an accessible and complete learning experience, translations must preserve all modalities: text for reading, slides for visual understanding, and speech for auditory learning. We present \textbf{BOOM}, a multimodal multilingual lecture companion that jointly translates lecture audio and slides to produce synchronized outputs across three modalities: translated text, localized slides with preserved visual elements, and synthesized speech. This end-to-end approach enables students to access lectures in their native language while aiming to preserve the original content in its entirety. Our experiments demonstrate that slide-aware transcripts also yield cascading benefits for downstream tasks such as summarization and question answering. The demo video and code can be found at https://ai4lt.github.io/boom/ \footnote{All released code and models are licensed under the MIT License}.
The rapid adoption of generative artificial intelligence (GenAI) technologies has led many organizations to integrate AI into their products and services, often without considering user preferences. Yet, public attitudes toward AI use, especially in impactful decision-making scenarios, are underexplored. Using a large-scale two-wave survey study (n_wave1=1514, n_wave2=1488) representative of the Swiss population, we examine shifts in public attitudes toward AI before and after the launch of ChatGPT. We find that the GenAI boom is significantly associated with reduced public acceptance of AI (see Figure 1) and increased demand for human oversight in various decision-making contexts. The proportion of respondents finding AI "not acceptable at all" increased from 23% to 30%, while support for human-only decision-making rose from 18% to 26%. These shifts have amplified existing social inequalities in terms of widened educational, linguistic, and gender gaps post-boom. Our findings challenge industry assumptions about public readiness for AI deployment and highlight the critical importance of aligning technological development with evolving public preferences.
This paper presents an autonomous control framework for articulated boom cranes performing prefabricated block assembly in construction environments. The key challenge addressed is precise placement control under passive joint dynamics that cause pendulum-like sway, complicating the accurate positioning of building components. Our integrated approach combines real-time vision-based pose estimation of building blocks, collision-aware B-spline path planning, and nonlinear model predictive control (NMPC) to achieve autonomous pickup, placement, and obstacle-avoidance assembly operations. The framework is validated on a laboratory-scale testbed that emulates crane kinematics and passive dynamics while enabling rapid experimentation. The collision-aware planner generates feasible B-spline references in real-time on CPU hardware with anytime performance, while the NMPC controller actively suppresses passive joint sway and tracks the planned trajectory under continuous vision feedback. Experimental results demonstrate autonomous block stacking and obstacle-avoidance assembly, with sway damping reducing settling times by more than an order of magnitude compared to uncontrolled passive dyna
Intelligent drill boom hole-seeking is a promising technology for enhancing drilling efficiency, mitigating potential safety hazards, and relieving human operators. Most existing intelligent drill boom control methods rely on a hierarchical control framework based on inverse kinematics. However, these methods are generally time-consuming due to the computational complexity of inverse kinematics and the inefficiency of the sequential execution of multiple joints. To tackle these challenges, this study proposes an integrated drill boom control method based on Reinforcement Learning (RL). We develop an integrated drill boom control framework that utilizes a parameterized policy to directly generate control inputs for all joints at each time step, taking advantage of joint posture and target hole information. By formulating the hole-seeking task as a Markov decision process, contemporary mainstream RL algorithms can be directly employed to learn a hole-seeking policy, thus eliminating the need for inverse kinematics solutions and promoting cooperative multi-joint control. To enhance the drilling accuracy throughout the entire drilling process, we devise a state representation that comb
Late 2023 witnessed significant user activity on EVM chains, resulting in a surge in transaction activity and putting many rollups into the first live test. While some rollups performed well, some others experienced downtime during this period, affecting transaction finality time and gas fees. To address the lack of empirical research on rollups, we perform the first study during a heightened activity during the late 2023 transaction boom, as attributed to inscriptions - a novel technique that enables NFT and ERC-20 token creation on Bitcoin and other blockchains. We observe that minting inscription-based meme tokens on zkSync Era allows for trading at a fraction of the costs, compared to the Bitcoin or Ethereum networks. We also found that the increased transaction activity, over 99% attributed to the minting of new inscription tokens, positively affected other users of zkSync Era, resulting in lowered gas fees. Unlike L1 blockchains, ZK rollups may experience lower gas fees with increased transaction volume. Lastly, the introduction of blobs - a form of temporary data storage - decreased the gas costs of Ethereum rollups, but also raised a number of questions about the security o
State capacity may shape whether natural resources generate prosperity, as it determines if windfalls are effectively turned into useful projects or wasted. We test this hypothesis studying the 2004-2011 mining boom in Peru, where mines' profits are redistributed as windfall transfers to local governments. Our empirical strategy combines geological data with the central government's mining windfalls allocation formula to identify the windfalls' effects on household incomes and other measures of economic development. Proxying local state capacity with the ability to tax and relying on a triple difference strategy we uncover significant variation in treatment response, with positive effects of windfalls limited to high state capacity localities. We find suggestive evidence that only localities with high state capacity succeed at transforming windfalls into infrastructure stocks, which in turns contributes to structural transformation and market integration. Lastly, social unrest increases in low state capacity localities that receive windfalls but fail to perceive their benefits. Our findings underscore important complementarities between investments in extractive industries and in s
Verlinde presents the gravitational force as due to gradients of entropy, an emergent force, with far reaching consequences. Using the Hawking-Bekenstein entropy formulation, we arrive at the conclusion that the Mass-Boom effect, presented elsewhere, forces the entropy of the universe to increase. Then the Mass-Boom is directly related to the existence of gravity. The principle of Mach implies that the Mass-Boom is responsible for the expansion of the universe. Thus, the Mass-Boom effect is a necessary condition for: 1) the increase of entropy with time, 2) the existence of gravity, and 3) for the expansion of the universe. The universe seems to initially appear and grow out of polarization: positive mass-boom (energy) versus negative gravitational potential energy boom, adding both always to zero. Polarization is then the cause of creation and evolution of the universe.
The bistable deployable composite boom (Bi-DCB) can achieve bistable function by storing and releasing strain energy, which has a good application prospect in space field. For example, it serves as the main support section of deployable structures (e.g., solar arrays and antennas). This paper investigates the folding stable state of the Bi-DCB through the analytical method. Based on the classical Archimedes' helix, the geometrical model of the Bi-DCB was established. Using energy principle, an analytical model for predicting the folding stable state of the Bi-DCB was presented. The failure indices of six Bi-DCBs in the folding stable state were calculated using the Tsai-Hill criterion and the maximum stress criterion. To validate the analytical model proposed in this paper, the prediction results were compared with the results of two Finite Element Models (FEMs) and experimental results, and the four were in good agreement. Finally, the effect of geometric parameters (i.e., radius of cross-section, thickness and length) on the folding stable state of the Bi-DCB was further investigated with the aid of the analytical model. It is shown that geometric parameters are one of the key fa
The bistable deployable composite boom (Bi-DCB) can realize the bistable function by storing and releasing strain energy, which has a good application prospect in the aerospace field. In this paper, the folding stable state of the Bi-DCB was investigated using experimental and numerical approaches. Using the vacuum bag method, six Bi-DCB specimens were prepared. Bistable experiments of Bi-DCB specimens were conducted and linear fitting with Archimedes' helix was performed to determine the folding stable configuration. In addition, two Finite Element Models (FEMs) were established for predicting the folding stable state of the Bi-DCB. Two classical failure criteria were utilized to analyze the stress level of the folding stable state of the Bi-DCB. Numerical results of two FEMs agreed well with experimental results, including the bistable deformation process and the folding stable state.
The processes and mechanisms underlying the origin and maintenance of biological diversity have long been of central importance in ecology and evolution. The competitive exclusion principle states that the number of coexisting species is limited by the number of resources, or by the species' similarity in resource use. Natural systems such as the extreme diversity of unicellular life in the oceans provide counter examples. It is known that mathematical models incorporating population fluctuations can lead to violations of the exclusion principle. Here we use simple eco-evolutionary models to show that a certain type of population dynamics, boom-bust dynamics, can allow for the evolution of much larger amounts of diversity than would be expected with stable equilibrium dynamics. Boom-bust dynamics are characterized by long periods of almost exponential growth (boom) and a subsequent population crash due to competition (bust). When such ecological dynamics are incorporated into an evolutionary model that allows for adaptive diversification in continuous phenotype spaces, desynchronization of the boom-bust cycles of coexisting species can lead to the maintenance of high levels of dive
Automation of cranes can have a direct impact on the productivity of construction projects. In this paper, we focus on the control of one of the most used cranes, the boom crane. Tower cranes and overhead cranes have been widely studied in the literature, whereas the control of boom cranes has been investigated only by a few works. Typically, these works make use of simple models making use of a large number of simplifying assumptions (e.g. fixed length cable, assuming certain dynamics are uncoupled, etc.) A first result of this paper is to present a fairly complete nonlinear dynamic model of a boom crane taking into account all coupling dynamics and where the only simplifying assumption is that the cable is considered as rigid. The boom crane involves pitching and rotational movements, which generate complicated centrifugal forces, and consequently, equations of motion highly nonlinear. On the basis of this model, a control law has been developed able to perform position control of the crane while actively damping the oscillations of the load. The effectiveness of the approach has been tested in simulation with realistic physical parameters and tested in the presence of wind distu
The European programs for development of supersonic air-flights involve new studies on the human perception of sonic boom. Because this noise includes high-level components at very low-frequency, the usual psycho-acoustic tests with headphones are not relevant; instead, the original sound-field can be reproduced with many loudspeakers in a small room, but the loudspeakers must be controlled for an accurate reproduction, both in time and space, in an area large enough to enclose a listener's head. In this paper, Active Noise Control is applied to sonic boom reproduction through Boundary Surface Control (as named by S.Ise) of the acoustic pressure around a listener. A small room was built at LMA with sixteen powerful low-frequency acoustic sources in the walls. Frequency and time-domain numerical simulations of sonic boom reproduction in this room are given, including a sensitivity study of the coupling between a listener's head and the incident sonic boom wave which combine into the effective sound-field to be reproduced.
The softening of a Gamma Ray Burst (GRB) afterglow bears remarkable similarities to the frequency evolution in a sonic boom. At the front end of the sonic boom cone, the frequency is infinite, much like a GRB. Inside the cone, the frequency rapidly decreases to infrasonic ranges and the sound source appears at two places at the same time, mimicking the double-lobed radio sources. Although a "luminal" boom violates the Lorentz invariance and is therefore forbidden, it is tempting to work out the details and compare them with existing data. This temptation is further enhanced by the observed superluminality in the celestial objects associated with radio sources and some GRBs. In this article, we calculate the temporal and spatial variation of observed frequencies from a hypothetical luminal boom and show remarkable similarity between our calculations and current observations.