The past 50 yr of advances in weed recognition technologies have poised site-specific weed control (SSWC) on the cusp of requisite performance for large-scale production systems. The technology offers improved management of diverse weed morphology over highly variable background environments. SSWC enables the use of nonselective weed control options, such as lasers and electrical weeding, as feasible in-crop selective alternatives to herbicides by targeting individual weeds. This review looks at the progress made over this half-century of research and its implications for future weed recognition and control efforts; summarizing advances in computer vision techniques and the most recent deep convolutional neural network (CNN) approaches to weed recognition. The first use of CNNs for plant identification in 2015 began an era of rapid improvement in algorithm performance on larger and more diverse datasets. These performance gains and subsequent research have shown that the variability of large-scale cropping systems is best managed by deep learning for in-crop weed recognition. The benefits of deep learning and improved accessibility to open-source software and hardware tools has been evident in the adoption of these tools by weed researchers and the increased popularity of CNN-based weed recognition research. The field of machine learning holds substantial promise for weed control, especially the implementation of truly integrated weed management strategies. Whereas previous approaches sought to reduce environmental variability or manage it with advanced algorithms, research in deep learning architectures suggests that large-scale, multi-modal approaches are the future for weed recognition.

How well are voters represented and what explains how well voters are represented? Answering these questions requires unpacking the mechanisms by which voters choose elected officials and elected officials implement policies. Though spatial theories of voting and legislative bargaining provide a broad framework for understanding these mechanisms, testing these theories involves a measurement problem where multiple political actors and outcomes must be located. We develop a technique for estimating policy outcomes, status quo locations, the ideology of elite political actors, and the ideology of voters, on a common scale. Using our new estimates, we demonstrate a similar level of incongruence for tax policies and spending policies. The incongruence arises for different reasons however—tax policies are over-responsive to the position of the median voter. Contrarily, spending policies are under-responsive and barely correlated with the position of the median voter. In examining the underlying mechanisms for policy change, we find that while the positions of elected officials over-respond to the median voter, the changing composition of state government has little immediate impact on policy. Instead, policy outcomes respond to long-term trends in the composition of government.

Australian conservation cropping systems are practiced on very large farms (approximately 3,000 ha) where herbicides are relied on for effective and timely weed control. In many fields, though, there are low weed densities (e.g., <1.0 plant 10 m−2) and whole-field herbicide treatments are wasteful. For fallow weed control, commercially available weed detection systems provide the opportunity for site-specific herbicide treatments, removing the need for whole-field treatment of fallow fields with low weed densities. Concern about the sustainability of herbicide-reliant weed management systems remain and there has not been interest in the use of weed detection systems for alternative weed control technologies, such as targeted tillage. In this paper, we discuss the use of a targeted tillage technique for site-specific weed control in large-scale crop production systems. Three small-scale prototypes were used for engineering and weed control efficacy testing across a range of species and growth stages. With confidence established in the design approach and a demonstrated 100% weed-control potential, a 6-m wide pre-commercial prototype, the “Weed Chipper,” was built incorporating commercially available weed-detection cameras for practical field-scale evaluation. This testing confirmed very high (90%) weed control efficacies and associated low levels (1.8%) of soil disturbance where the weed density was fewer than 1.0 plant 10 m−2 in a commercial fallow. These data established the suitability of this mechanical approach to weed control for conservation cropping systems. The development of targeted tillage for fallow weed control represents the introduction of site-specific, nonchemical weed control for conservation cropping systems.

The widespread use of herbicides in cropping systems has led to the evolution of resistance in major weeds. The resultant loss of herbicide efficacy is compounded by a lack of new herbicide sites of action, driving demand for alternative weed control technologies. While there are many alternative methods for control, identifying the most appropriate method to pursue for commercial development has been hampered by the inability to compare techniques in a fair and equitable manner. Given that all currently available and alternative weed control methods share an intrinsic energy consumption, the aim of this review was to compare methods based on energy consumption. Energy consumption was compared for chemical, mechanical, and thermal weed control technologies when applied as broadcast (whole-field) and site-specific treatments. Tillage systems, such as flex-tine harrow (4.2 to 5.5 MJ ha−1), sweep cultivator (13 to 14 MJ ha−1), and rotary hoe (12 to 17 MJ ha−1) consumed the least energy of broadcast weed control treatments. Thermal-based approaches, including flaming (1,008 to 4,334 MJ ha−1) and infrared (2,000 to 3,887 MJ ha−1), are more appropriate for use in conservation cropping systems; however, their energy requirements are 100- to 1,000-fold greater than those of tillage treatments. The site-specific application of weed control treatments to control 2-leaf-stage broadleaf weeds at a density of 5 plants m−2 reduced energy consumption of herbicidal, thermal, and mechanical treatments by 97%, 99%, and 97%, respectively. Significantly, this site-specific approach resulted in similar energy requirements for current and alternative technologies (e.g., electrocution [15 to 19 MJ ha−1], laser pyrolysis [15 to 249 MJ ha−1], hoeing [17 MJ ha−1], and herbicides [15 MJ ha−1]). Using similar energy sources, a standardized energy comparison provides an opportunity for estimation of weed control costs, suggesting site-specific weed management is critical in the economically realistic implementation of alternative technologies.

We examined the prospective associations of objective and subjective measures of stress during pregnancy with infant stress reactivity and regulation, an early-life predictor of psychopathology. In a racially and ethnically diverse low-income sample of 151 mother–infant dyads, maternal reports of stressful life events (SLE) and perceived stress (PS) were collected serially over gestation and the early postpartum period. Infant reactivity and regulation at 6 months of age was assessed via maternal report of temperament (negativity, surgency, and regulation) and infant parasympathetic nervous system physiology (respiratory sinus arrhythmia [RSA]) during the Still Face Paradigm. Regression models predicting infant temperament showed higher maternal prenatal PS predicted lower surgency and self-regulation but not negativity. Regression models predicting infant physiology showed higher numbers of SLE during gestation predicted greater RSA reactivity and weaker recovery. Tests of interactions revealed SLE predicted RSA reactivity only at moderate to high levels of PS. Thus, findings suggest objective and subjective measures of maternal prenatal stress uniquely predict infant behavior and physiology, adjusting for key pre- and postnatal covariates, and advance the limited evidence for such prenatal programming within high-risk populations. Assessing multiple levels of maternal stress and offspring stress reactivity and regulation provides a richer picture of intergenerational transmission of adversity.

The goal of this study was to compare clinical and psycholinguistic approaches to the measurement of verbal behavior according to their ability to discriminate schizophrenic (n = 11) from manic-depressive (n = 11) and normal control (n = 11) subjects. Language samples were obtained during Rorschach testing and analyzed using a clinical measure, the Thought Disorder Index (TDI), and a battery of linguistic measures. Five linguistic categories were examined: productivity, repetition, dysfluency, cohesiveness, and syntactic complexity. On the clinical measure, schizophrenic subjects had high thought disorder scores. On the linguistic measures, the speech of schizophrenics differed significantly from the other two groups in productivity and in the number of clearly referenced elements, a measure of cohesion. This measure of cohesion was positively correlated with TDI scores. The findings are discussed in terms of the benefits of using more objective and quantitative measurements in research on thought disorganization in schizophrenia.

The straw itch mite, Pyemotes tritici Lagrèze-Fossat and Montané (Acari: Pyemotidae), was discovered parasitising the goldspotted oak borer, Agrilus auroguttatus Schaeffer (Coleoptera: Buprestidae), an invasive exotic species to California, United States of America, and the Mexican goldspotted oak borer, Agrilus coxalis Waterhouse (Coleoptera: Buprestidae), during surveys for natural enemies for a classical biological control programme for A. auroguttatus. Pyemotes tritici caused low levels of mortality to each species of flatheaded borer, but it will likely not be a good candidate for a biological control programme because it is a generalist parasitoid with deleterious human health effects.

Resilience after a nuclear power plant or other radiation emergency requires response and recovery activities that are appropriately safe, timely, effective, and well organized. Timely informed decisions must be made, and the logic behind them communicated during the evolution of the incident before the final outcome is known. Based on our experiences in Tokyo responding to the Fukushima Daiichi nuclear power plant crisis, we propose a real-time, medical decision model by which to make key health-related decisions that are central drivers to the overall incident management. Using this approach, on-site decision makers empowered to make interim decisions can act without undue delay using readily available and high-level scientific, medical, communication, and policy expertise. Ongoing assessment, consultation, and adaption to the changing conditions and additional information are additional key features. Given the central role of health and medical issues in all disasters, we propose that this medical decision model, which is compatible with the existing US National Response Framework structure, be considered for effective management of complex, large-scale, and large-consequence incidents. (Disaster Med Public Health Preparedness. 2012;0:1-10)

We present a new unified dataset of common-space ideal points, committee assignments, and financial interests for all state legislators in 1999. We describe the compilation of the dataset and offer a few possible applications.