The design-based paradigm may be adopted in causal inference and survey sampling when we assume Rubin’s stable unit treatment value assumption (SUTVA) or impose similar frameworks. While often taken for granted, such assumptions entail strong claims about the data-generating process. We develop an alternative design-based approach: we first invoke a generalized, non-parametric model that allows for unrestricted forms of interference, such as spillover. We define an associated set of inferential targets and discuss their interpretation under SUTVA and a weaker assumption that we call the “no unmodeled revealable variation assumption” (NURVA). We then reconstruct the standard paradigm, reconsidering SUTVA at the end rather than assuming it at the beginning. Despite its similarity to SUTVA, we demonstrate the practical limitations of NURVA alone for identifying substantively interesting quantities. In so doing, we provide clarity on the nature and importance of SUTVA for applied research.

Fall panicum is a major annual grass weed in drill-seeded rice production systems on organic soils in Florida. Field studies conducted in 2021 and 2022 aimed to determine the effects of season-long fall panicum interference on rough rice yield on organic soils. The relationship between fall panicum density and percent rough rice yield loss relative to the weed-free control was described using the rectangular hyperbola model. Yield loss increased with increasing fall panicum density. The initial slope parameter (I), representing percent yield loss as density approaches zero, was estimated at 9.7%, indicating that even very low fall panicum densities caused measurable yield reductions. The asymptote parameter (A), representing percent yield loss as density approaches infinity, was estimated at 118%, suggesting that yield loss approaches 100% at very high fall panicum densities within the biological limits of the crop-weed interference. Within the observed density range (1 to 49 plants m−2), yield loss was predicted to increase from approximately 9% to 95%, and 50% yield loss was estimated to occur at 9 plants m−2. Thousand-grain weight declined linearly with increasing fall panicum density, with each additional fall panicum plant per square meter reducing thousand-grain weight by 0.18 g, indicating that fall panicum interference affected grain filling. These results demonstrate that fall panicum is highly competitive with rice and that even low infestations can result in substantial yield loss, showing the importance of timely and effective management in Florida rice organic soil production systems.

The performance of elite athletes is at the forefront of attention in sports science, with a predominant focus on technical, physiological, mental, or contextual factors that can be leveraged to optimize athlete performance. Athletes’ off-sports activities, however, remain largely unexplored. What is it that elite athletes do in their off-sports time, and how are their off-sports activities related to sports experiences and outcomes? With this qualitative study, we aim to illuminate athletes’ off-sports activities and their implications for sports outcomes. We collected listings of off-sports activities from 46 professional soccer players and interviewed 15 elite speed skaters about their off-sports activities and their potential to enrich or interfere with their sports domain. The resulting category framework of off-sports activities comprises eight categories, reflecting social, cognitive, and physical off-sports activities. Next, the speed skaters described beneficial spillover experiences for active, high-effort mastery-oriented off-sports activities. However, such activities could also cause fatigue if not balanced with sufficient, more passive, restful activities. In all, athletes did experience spillover from non-sports to sports and their qualitative accounts reveal several antecedents, mechanisms, and outcomes of spillover, supporting the process view of the work–home resources (W-HR) model (Ten Brummelhuis & Bakker, 2012).

There is a high demand for cultural weed management strategies targeting Palmer amaranth (Amaranthus palmeri S. Watson) in furrow-irrigated rice (Oryza sativa L.) production due to overreliance on herbicides and the lack of a continual flood to prevent weed emergence. Amaranthus palmeri has been shown to reduce corn (Zea mays L.), cotton (Gossypium hirsutum L.), and soybean [Glycine max (L.) Merr.] yields when it interferes with the crop. However, minimal research has been conducted to assess the ability of this weed species to impact rice grain yield. The manipulation of rice seeding rate to enhance rice canopy formation and favor the crop over the weed has not been fully explored. Hence, research was conducted to (1) evaluate the effect of rice density on A. palmeri emergence, rice canopy cover, and relative yield; and (2) determine the impact of A. palmeri density and aboveground biomass on rice grain yield. A natural population of A. palmeri was allowed to emerge at varying densities throughout the growing season within furrow-irrigated rice. Amaranthus palmeri plants caused 12% to 87% yield loss at densities ranging from 1 to 20 plants m−2, and yield loss was 45% to 80% for plants weighing 200 to 800 g m−2. When furrow-irrigated rice was sown at various densities, crop canopy cover increased as rice plant density increased. Most A. palmeri emergence occurred within the first 4 wk after rice emergence, before canopy formation could have an effect. Amaranthus palmeri emergence beyond 5 wk after rice emergence decreased as rice plant density and canopy cover increased. These results indicate that A. palmeri has the potential to cause severe yield loss and that residual herbicides will be vital for A. palmeri management in a furrow-irrigated rice system, due to the continual emergence of weeds up to crop canopy formation.

The critical period for weed control (CPWC) has been used to define weed-control threshold triggers in many cropping systems. Using the CPWC to develop a weed-control threshold for broadleaf weeds that emerge later in the season would be valuable to cotton growers to enable them to schedule management of later emerging weeds to occur before crops suffer unacceptable yield losses. Field studies were conducted over two seasons from 2006 to 2008 to determine the CPWC for a broadleaf weed in cotton, using mungbean as a mimic weed. Mungbean was planted into cotton at densities of 1 to 50 plants m−2, at up to 450 growing-degree days (GDD) after crop planting, and removed at successive 200 GDD intervals after introduction, or left to compete full season. The data were fit to logistic and Gompertz curves. More complex models were developed and tested that included the time of planting and removal, weed density, height and biomass in the relationships. The CPWC models were able to predict the yield loss from later emerging weeds and together with an understanding of the expected growth rates of the weeds, the functions could be used predictively to determine the likely impact of delaying a weed-control input. This predictive element will be of value to cotton growers needing to coordinate weed-control inputs with other farm activities.

Typically, weed density is used to predict weed-induced yield loss, as it is easy and quick to quantify, even though it does not account for weed size and time of emergence relative to the crop. Weed–crop leaf area relations, while more difficult to measure, inherently account for differences in plant size, representing weed–crop interference more accurately than weed density alone. Unmanned aerial systems (UASs) may allow for efficient quantification of weed and crop leaf cover over a large scale. It was hypothesized that UAS imagery could be used to predict maize (Zea mays L.) yield loss based on weed–crop leaf cover ratios. A yield loss model for maize was evaluated for accuracy using 15- and 30-m-altitude aerial red–green–blue and four-band multispectral imagery collected at four North Carolina locations. The model consistently over- and underpredicted yield loss when observed yield loss was less than and greater than 3,000 kg ha−1, respectively. Altitude and sensor type did not influence the accuracy of the prediction. A correction for the differences between predicted and observed yield loss was incorporated into the linear model to improve overall precision. The correction resulted in r2 increasing from 0.17 to 0.97 and a reduction in root mean-square error from 705 kg ha−1 to 219 kg ha−1. The results indicated that UAS images can be used to develop predictive models for weed-induced yield loss before canopy closure, making it possible for growers to plan production and financial decisions before the end of the growing season.

This paper presents a comprehensive analysis of interference events in automotive scenarios based on radar systems equipped with communication-assisted chirp sequence (CaCS). First, it examines the impact of interference on radar and communication functionalities in CaCS systems according to the orientation of the investigated nodes. For this purpose, a graph-based approach is employed with MATLAB simulations to illustrate the potential occurrence of interference on the graph for communication functionality compared with their counterparts on radar. Second, the paper delves into the impact of interference on the synchronization between two communicating CaCS nodes. It extends a previous study to match the frequency of current radar sensors, where chirp estimation, an adjusted version of the Schmidl & Cox algorithm, and correlation are adopted to synchronize the transmitter and receiver of two CaCS communicating nodes in the time-frequency plane. The proposed synchronization method is finally verified by measurements at ${79}\,\mathrm{GHz}$ with a system-on-chip, where the resulting correlation metric and mean square error are illustrated as validation factors.

The presented research investigates the impact of interference on the performance of aircraft Global Navigation Satellite System (GNSS) receivers with a specific focus on the behaviour of Automatic Dependent Surveillance-Broadcast (ADS-B) position quality indicators. Several experiments were performed with different aircraft types, such as Airbus, Boeing, Beechcraft King Air B350 or Tecnam, and using various intensities of GNSS jamming. The behaviour of various quality indicators, such as the Navigation Integrity Category, Navigation Accuracy Category, Source Integrity Level and System Design Assurance transmitted in different types of ADS-B messages, is analysed. We investigate not only situations where the quality indicators drop to zero, but also the complete evolution of the changes in the indicators as a function of the increasing power of the jamming signal. Based on the analysis of changes in the ADS-B quality indicators, the estimation of the most likely interference signal power required to discontinue the tracking of an already acquired GPS L1 Coarse/Acquisition signal is made. Additionally, the interference signal power to prevent re-acquisition is also estimated. The findings improve the understanding of interference effects and can support the development of robust interference mitigation techniques in aviation applications.

Furrow-irrigated rice (Oryza sativa L.) hectares are increasing in the Midsouth. The lack of sustained flooding creates a favorable environment for weed emergence and persistence, which makes Palmer amaranth (Amaranthus palmeri S. Watson) difficult to control throughout the growing season. The negative yield impacts associated with season-long A. palmeri interference in corn (Zea mays L.), cotton (Gossypium hirsutum L.), and soybean [Glycine max (L.) Merr.] have been evaluated. However, there is limited knowledge of the weed’s ability to influence rice grain yield. Research was initiated in 2022 and 2023 to determine the effect of A. palmeri time of emergence relative to rice on weed seed production and grain yield. Cotyledon-stage A. palmeri plants were marked every 7 d, beginning 1 wk before rice emergence through 4 wk after rice emergence. Amaranthus palmeri seed production decreased exponentially as emergence timing was delayed relative to rice, and seed production increased by 447 seed plant−1 for every 1-g increase in weed biomass. Without rice competition and from the earliest emergence timing, A. palmeri produced 540,000 seeds plant−1. Amaranthus palmeri that emerged 1 wk before the crop had the greatest spatial influence on rice, with grain yield loss of 5% and 50% at a distance of 1.4 m and 0.40 m from the weed, respectively. As A. palmeri emergence was delayed, the area of influence decreased. However, A. palmeri plants emerging 3.5 wk after rice emergence still negatively affected grain yield and produced sufficient seed to replenish the soil seedbank, potentially impacting long-term crop management decisions. These results show that the time of A. palmeri emergence is a crucial factor influencing rice grain yield and weed seed production, which can be used to determine the consequences of escapes in rice.