Oklahoma State University System

Vascular streak dieback (VSD) poses a significant threat to the sustainability of woody ornamentals in the United States, particularly Cercis spp. (redbud). This study aimed to develop reliable molecular diagnostic tools for detecting Ceratobasidium sp. D.P. Rogers (Csp), the fungus consistently detected from VSD-symptomatic redbuds and other woody ornamentals. Molecular markers can provide rapid and accurate detection for fastidious fungi such as Csp that are challenging to recover in culture media. However, current molecular tools for Csp detection lack sensitivity and specificity. To address this, specific PCR and quantitative PCR (qPCR) primers were developed, targeting the internal transcribed spacer (ITS) genetic region. The existing primers for the second-largest subunit of RNA polymerase II (RPB2), translation elongation factor 1-alpha (TEF1-α), and ATP synthase subunit 6 (ATP6) genetic regions of Rhizoctonia fungi were also redesigned to enhance the sensitivity and accuracy of detection for Csp. The CP-qP-Csp primer set, with a detection limit of 86.02 pg/μl, effectively detected Csp in VSD-symptomatic tissues, cultures, and soils and is recommended for molecular detection of Csp from woody ornamentals in the U.S. Cross-platform validation across seven laboratories confirmed the robustness of the assays. These findings provide nursery producers with critical early-detection capabilities, potentially preventing significant economic losses and contributing to the sustainability of woody ornamental production. Future research will focus on refining these assays for field applications and exploring the genetic characteristics of Csp across different hosts and pathogen interactions to better understand the epidemiology of VSD.

Corn stunt is one of the most significant corn diseases in the Neotropics, leading to severe plant stunting and substantial yield losses. Although four pathogens have been found either singly or in combination in infected plants in the Americas, the corn stunt spiroplasma (Spiroplasma kunkelii) have been the most predominant pathogen associated with the disease in the U.S., due to its widespread distribution in the Rio Grande Valley region and persistent occurrence in California and Florida. During the 2024 growing season, reports of chlorosis, leaf reddening, and stunting in corn fields in Southern, Great Plains, Central Corn Belt, and Northeastern states raised concern regarding the possibility of a more widespread distribution of corn stunt spiroplasma in the U.S. Symptomatic corn leaf samples were collected in commercial and experimental field sites across the U.S. Detection and identification of S. kunkelii were performed using a polymerase chain reaction assay targeting a section of the spiralin gene, followed by amplicon sequencing. This study provides the first report of the pathogen S. kunkelii associated with corn stunt symptoms distributed across six counties in Oklahoma, 14 counties in Kansas, two counties in Missouri and Arkansas, four counties in New York, and one county in each of Nebraska, South Dakota, Wisconsin, Minnesota, Indiana, and Alabama. All states with submitted samples had at least one confirmed case of S. kunkelii.

Purpose This study examines the effect of horizontal and vertical foreign direct investment (FDI) linkages on the investment decisions of domestic plants. It explores this link for plants in a developing country, where the dynamics of FDI spillovers on domestic investment choices are distinct from those in developed countries. It also adds to the literature by examining the role the absorptive capacity of plants plays in this nexus, enriching our understanding of the interaction between FDI and the investment choices of domestic plants and providing insights for policymakers and managers seeking to maximize the positive effects of FDI spillovers. Design/methodology/approach This study specifies the dynamic investment equation using the Euler and Q models. This equation is estimated using the first-difference and system generalized method of moments GMM estimators, which allow us to address persistency, endogeneity and unobserved plant-specific effects. FDI exposure proxy, calculated by weighing the FDI engagement variable by firm size, captures the strength of foreign equity participation in an industry and time. Input–output (IO) tables are used to calculate the proxies for horizontal and vertical (backward and forward) FDI. Total factor productivity is calculated using a method advocated by Olley and Pakes (1996) that allows us to control for selection and simultaneity. Findings We find that FDI inflows into both the domestic plants’ own industry and the input-supplying industry significantly boost the capital accumulation of the average domestic plants. Differentiating plants based on their absorptive capacities reveals that the presence of foreign firms significantly increases capital deepening for domestic plants with high absorptive capacity within their own industry (horizontal linkage) and in industries that supply inputs to the FDI-exposed industry (backward linkage). However, it leads to a decrease in capital deepening for high absorptive capacity domestic plants in industries using inputs produced by the industry exposed to FDI (forward linkage). Research limitations/implications These findings have implications for policymakers and managers who aim to design incentives to maximize the positive spillover effect of horizontal and vertical FDI linkages on the capital deepening of domestic plants. Exploring additional mechanisms that could play a role in this nexus, separately for plants in different industries (such as the industries in which the country has a comparative advantage), may be fruitful. Originality/value To our knowledge, the relationship between the investment behavior of the domestic plants and FDI linkages has not been examined for a developing country. It is useful to explore this link in developing countries, as the investment choices of plants in these nations may be impacted differently by FDI spillovers than those in advanced nations. This study further contributes to the literature by investigating whether the plants’ absorptive capacity affects the nexus between FDI linkages and investment decisions of the domestic plants. The findings are valuable for policymakers and managers who aim to increase plants’ capital accumulation in developing countries.

Recent declines in eastern wild turkeys (Meleagris gallopavo silvestris) has prompted increased interest in management and research of this important game species. However, the mechanisms underlying these declines are unclear, leaving uncertainty in how best to manage this species. Foundational to effective management of wildlife species is understanding the biotic and abiotic factors that influence demographic parameters and the contribution of vital rates to population growth. Our objectives for this study were to: 1) conduct a literature review to collect all published vital rates for eastern wild turkey over the last 50 years, 2) perform a scoping review of the biotic and abiotic factors that have been studied relative to wild turkey vital rates and highlight areas that require additional research, and 3) use the published vital rates to populate a life-stage simulation analysis (LSA) and identify the vital rates that make the greatest contribution to population growth. Based on published vital rates for eastern wild turkey, we estimated a mean asymptotic population growth rate (λ) of 0.91 (95% CI = 0.71, 1.12). Vital rates associated with after second year (ASY) females were most influential in determining population growth. Survival of ASY females had the greatest elasticity (0.53), while reproduction of ASY females had lower elasticity (0.21), but high process variance, causing it to explain a greater proportion of variance in λ. Our scoping review found that most research has focused on the effects of habitat characteristics at nest sites and the direct effects of harvest on adult survival, while research on topics such as disease, weather, predators, or anthropogenic activity on vital rates have received less attention. We recommend that future research take a more mechanistic approach to understanding variation in wild turkey vital rates as this will assist managers in determining the most appropriate management approach.

Lithium ion cells have been a major energy source of Electric Vehicles (EVs) for recent years. Multiple cells connected in series and parallel make a complete battery pack to drive EVs. Lack of information about the battery's safe operating points can lead to degradation of cell's life. Therefore, it is important to supervise the states of a lithium ion cell, particularly its State of Charge (SoC) and the effect of EV's speed on open circuit voltage. The Open Circuit Voltage (OCV) is crucial for SoC estimation and for the effect of speed on battery cells. In this paper, the implementation of pulse discharge test at room temperature is presented to measure states of lithium ion cell. In addition, an induction's motor model with a current controller is designed for EV's speed estimation and for the establishment of a relationship between speed and the current drawn by the vehicle. The experiments are conducted on Lithium iron phosphate cells to establish a relation between OCV and SoC. The results verify that as the load current increases, the new open circuit voltage level established will be low as compared to initial cell voltage level.

This paper proposes a novel approach which enables to generate a crypt image incorporating multiple gray images in special domain. It involves merging of two 8-bit images into a single 8-bit crypt image and applying the same technique for two merged 8-bit images which in turn generates a single 8-bit crypt image. The generated image is subjected to masking and compression. This approach ensures optimum resource utilization (data redundancy, bandwidth, traffic load) along with secure transmission.

Sound event recognition without the context is challenging for both humans and robots due to the diversity of sound events. Contextual information allows them to disambiguate the sound events, for example, in a home environment. This paper proposes and implements a context-aware sound event recognition for a home service robot that monitors the elderly living alone at home. The location context of sound events is estimated by fusing distributed PIR (Passive Infrared) Sensors. A two-level dynamic Bayesian network (DBN) is used to model the intra-temporal and inter-temporal constraints among the context and sound events. We conducted experiments in a robot-integrated smart home (RiSH) testbed to evaluate the proposed method. The obtained results show the effectiveness and accuracy of context-aware sound event recognition.

The ubiquitous use of cameras in a home environment raises privacy concerns, which is one of the major barriers to the deployment of smart home systems for elderly and disabled care. Social robots are equipped with cameras that can witness embarrassing situations of their owners, such as nakedness during a morning bath. In this paper, our proposed solution is to let the robot detect such privacy-sensitive moments and then turn away to avoid observing the person. We proposed a method based on the Convolutional Neural Networks (CNN) to detect nakedness in a daily living scenario. In this method, two CNNs are used: one detects whether a person is present or not in the current scene, and the other detects privacy-sensitive situations. We implemented the method on the ASCC home service robot developed in our lab. The CNNs were trained with a database composed of more than 2900 pictures collected from the Internet and from our ASCC Smart Home. In our experiments, we considered three human poses: sitting, standing, and lying. If the robot detects a privacy-sensitive situation, it will turn around and verbally announce its intention, which ensures the person that he or she is not being watched. Our experiments validate the proposed privacy preserving method. We also conducted a user study and find that people prefer that the robot takes some actions to avoid directly watching them in privacy-sensitive situations.

Certification of adaptive control algorithms for use on aerospace applications has not yet been accomplished in the aerospace industry. According to an emerging consensus between various authors, online monitoring and health assessment will play an integral role in closing this gap. In this paper we propose a monitoring system for Model Reference Adaptive Controllers, which enables online detection of future state requirement violations. We achieve this by employing Gaussian Process regression, which leads to a belief on the uncertainty in the system dynamics. Using analytic time-series forecasting, the system dynamics can be projected into the future, thus allowing for a statistical assertion whether a state requirement will be violated during the prediction horizon. We show the concept in numerical simulation.

Twitter is a powerful real-time micro-blogging service and a platform where users communicate with each other instantaneously. Thus, tweets form an integral part of big data ecosystem. While this platform serves as an efficient information diffusion medium, it can also be used to spread misinformation intentionally or unintentionally, which can damage the reputation of an individual or a corporation. Misinformation could also be harmful to society in general. As veracity in big data gains more attention, it is also important to develop methods to estimate veracity of tweets. There are no definitive measures to determine the veracity of tweets from tweets themselves. Other information that are required to verify tweets may not be readily available. Hence, there is a need for such mechanisms to determine the level of accuracy of tweets from available data. In this paper we propose three quantitative measures we name as topic diffusion, geographic dispersion, and spam index as indicators of veracity of tweets. These measures are derived from tweets themselves independent of any corroborating data. The proposed measures are tested using tweets about oil companies as validators. To validate the proposed measures, information extracted from tweets are compared with information collected from official data sources. Our experiments show that the proposed measures were able to estimate the level of veracity among tweets in most topics we tested. We also found the measures useful to compare the veracity of different topics as points in a 3-dimensional space. Another application of veracity indices to positions of political candidates is also described.