Bt corn and cotton planting may benefit peanut growers by reducing aflatoxin risk

Decades of studies have shown that Bt corn, by reducing insect damage, has lower levels of mycotoxins (fungal toxins), such as aflatoxin and fumonisin, than conventional corn. We used crop insurance data to infer that this benefit from Bt crops extends to reducing aflatoxin risk in peanuts: a non-Bt crop. In consequence, we suggest that any benefit–cost assessment of how transgenic Bt crops affect food safety should not be limited to assessing those crops alone; because the insect pest control offered by Bt crops affects the food safety profile of other crops grown nearby. Specifically, we found that higher Bt corn and Bt cotton planting rates in peanut-growing areas of the United States were associated with lower aflatoxin risk in peanuts as measured by aflatoxin-related insurance claims filed by peanut growers. Drought-related insurance claims were also lower: possibly due to Bt crops' suppression of insects that would otherwise feed on roots, rendering peanut plants more vulnerable to drought. These findings have implications for countries worldwide where policies allow Bt cotton but not Bt food crops to be grown: simply planting a Bt crop may reduce aflatoxin and drought stress in nearby food crops, resulting in a safer food supply through an inter-crop “halo effect.”This article is published as Yu, J., Hennessy, D.A. and Wu, F. (2024) Bt corn and cotton planting may benefit peanut growers by reducing aflatoxin risk. Plant Biotechnol. J., https://doi.org/10.1111/pbi.14425. © 2024 The Author(s). This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made

A Comprehensive Pytorch Framework to Benchmark CNN and ViT Models

Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have shown remarkable performance in computer vision tasks, including object detection and image recognition. These models have evolved significantly in architecture, efficiency, and versatility. Concurrently, deep learning frameworks have diversified, with versions that often complicate reproducibility and unified benchmarking. We propose ConVision Benchmark, a comprehensive framework in PyTorch, to standardize the implementation and evaluation of state-of-the-art CNN and ViT models. This framework addresses common challenges such as version mismatches and inconsistent validation metrics. As a proof of concept, we perform an extensive benchmark analysis on a COVID-19 dataset, encompassing nearly 200 CNN and ViT models in which DenseNet-161 and MaxViT-Tiny achieve exceptional accuracy with a peak performance of around 95%. Although we primarily used the COVID-19 dataset for image classification, the framework is adaptable to a variety of datasets, enhancing its applicability across different domains. Our methodology includes rigorous performance evaluations, highlighting metrics such as accuracy, precision, recall, F1 score, and computational efficiency (FLOPs, MACs, CPU, and GPU latency). The ConVision Benchmark facilitates a comprehensive understanding of model efficacy, aiding researchers in deploying high-performance models for diverse applications

Measurement and estimation of evapotranspiration in a maize field: A new method based on an analytical water flux model

Quantifying evapotranspiration (ET) in rainfed cropping systems can be challenging due to complicated interactions among site-specific soil, plant, and management factors. In Northeast China, ET and soil water status in maize fields often display strong spatial and temporal variations due to the changes in tillage practice, planting pattern, and maize plant density. Previous studies have shown that near-surface soil water content (θ) observations at multiple scales provide the potential to estimate surface soil water fluxes. In this study, we introduced a new method to estimate daily field ET by using a soil water flux model mainly based on the time-series of θ at a depth of 2.5 cm. The new method required a calibration of soil water diffusivity with maximum net water flux in the near-surface soil layer, which was related to precipitation redistribution below the canopy. Finally, the new method was evaluated using observed ET values over a 2-year period in a maize field, where independent measurements of soil water evaporation (E) and transpiration (T) were made with heat-pulse sensors and sap-flow gauges, respectively. Field observations showed that E dominated water loss during the seedling stage (16% of total ET). As the canopy was fully developed, E sharply decreased to a value of 0.4 mm d−1, and T accounted for about 89% of ET since the silking stage. The new method to estimate ET performed well in drying periods, while it tended to underestimate ET in wet periods with substantial infiltration into the surface layer. On rain-free days, the ET values estimated with the new method matched well with the measured E+T values, with R2 and RMSE values of 0.85 and 1.93 mm d−1. Therefore, the new approach provides an effective way to quantify maize ET.This article is published as Liu, Yutong, Yili Lu, Morteza Sadeghi, Robert Horton, and Tusheng Ren. "Measurement and estimation of evapotranspiration in a maize field: A new method based on an analytical water flux model." Agricultural Water Management 295 (2024): 108764. doi:10.1016/j.agwat.2024.108764. © 2024 The Author(s). This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/)

Effect of Breed, Sex and Age on Body Weight and Linear Body Measurements in Punjab Goat Breeds

Current study was performed to investigate the effect of i. breed, sex and age on body weight and morphometric measurement traits and ii. phenotypic correlation of morphometric measurement traits to body weight. Data on body weight and morphometric measurement traits i.e., live body weight, body length, wither height, pubic bone length, heart girth and chest width were collected from 792 individuals from seven goat breed of Punjab including five strains of Beetal breed; Barbari, Dera Din Pannah, Nachi, Teddi, Pahari and Pothwari. Body weight was measured in kilograms with the help of weighing balance and morphometric measurement traits were measured with measuring tape in centimeters (cm). Mixed Procedure with Restricted Maximum Likelihood methodology in SAS University Edition was used to investigate the effect of sex, breed and age on different body measurements of seven goat breeds under investigation. The findings revealed that sex had significant effect on body weight and morphometric measurement traits except for pubic bone length among all the studied goat breeds. There was also a significant effect of breed on body weight and body measurements. Beetal had a maximum adult weight (44.49±0.48 kg) followed by Nachi (37.45±2.32 kg), Daira Din Panah (37.24± 2.36 kg), Barbari (36.99±2.21 kg), Pothwari (34.25±2.67 kg), Teddi (23.52±1.55 kg) and Pahari (20.81±1.78 kg). A strong positive correlation of body weight with all morphometric measurements was observed i.e., to heart girth (0.895), to body length (0.833), to body height (0.789), to chest length (0.741) and to pubic bone length (0.664). It was concluded that variations in body measurements and body weight depended largely on age, sex and breed. The results from the current study might have possible implications in launching proper selection program for improving meat potential of native goat breeds.This article is published as Moaeen-ud-Din, Muhammad, Raja Danish Muner, Muhammad Sajjad Khan, and James M. Reecy. "Effect of Breed, Sex and Age on Body Weight and Linear Body Measurements in Punjab Goat Breeds." Pak. J. Agri. Sci 61, no. 1 (2024): 243-250. Copyright 2024 Pakistan Association of Advancement in Agricultural Sciences. This work is licensed as Attribution 4.0 International (CC BY 4.0)

Using socio-technical water systems in construction engineering to address water insecurity after Hurricanes Maria and Fiona

Water insecurity, characterized by insufficient, unreliable, and costly water necessary for a healthy life, is a significant issue in Puerto Rico, particularly affecting rural and low-income areas. The situation is worsened by historical challenges in water management, poor water infrastructure, and the impact of natural disasters like hurricanes, which intensify pre-existing water problems in vulnerable communities. Puerto Rico, an island frequently hit by hurricanes, experienced severe damage from Hurricanes Maria and Fiona in September 2017 and 2022, respectively. The island's financial crisis in recent years has hindered its capacity to invest in and maintain its water systems, leading to frequent disruptions in water supply and issues with water quality, including dangerous levels of lead and bacteria, presenting considerable health risks. Despite the extensive damage caused by hurricanes, recovery efforts have focused more on housing and roads than on water infrastructure, resulting in inadequate attention to water system restoration. The neglect of water infrastructure recovery has significant health, livelihood, and well-being implications worldwide. In Puerto Rico, water insecurity is associated with adverse health effects, including waterborne diseases like diarrhea and cholera, as well as skin conditions and diseases such as leptospirosis. Nevertheless, research is scarce on the specific complexities of water insecurity exacerbated by natural disasters and their impact on mental health and overall well-being in Puerto Rico. This dissertation presents an in-depth study focused on understanding the post-disaster environment in Puerto Rico after Hurricanes Maria and Fiona, with an emphasis on water insecurity. The proposal is structured into four main chapters, each exploring different aspects of water insecurity. Chapter 2 delves into the gap between knowledge and behavior regarding tap water consumption, posing the question, "Why do Puerto Rican consumers continue to consume tap water despite knowing or believing it to be unsafe?" The methodology involves conducting household surveys (N = 154) from May to July 2022, as well as in-depth interviews (N = 154) in the same period in the municipalities of Loíza, Comerío, and Aguas Buenas. The data collected from these surveys and interviews were analyzed using descriptive statistics and qualitative analysis. The findings identify four distinct groups of individuals based on their trust perceptions and behaviors towards drinking water. Two groups demonstrate alignment between their trust in tap water and their corresponding behavior—either they trust and drink tap water, or they mistrust it and avoid it. The other two groups exhibit a gap or misalignment, showing counterintuitive behavior; they might drink tap water despite mistrust or avoid it despite trust, influenced by socio-economic factors or long-term habits of using tap water without adverse health effects. Additionally, the findings reveal a general mistrust among consumers towards tap water, primarily attributed to its unsatisfactory quality over the past decade, as evidenced by its taste, color, and odor. Chapter 3 of the dissertation presents the development and initial psychometric evaluation of the Water Quality Perception Scale (WQPS). This chapter addresses a gap identified in the original work by Doria et al. (2009), where the items were treated as individual indicators. It aims to assess whether these items collectively represent a single, unified construct of water quality perception or if they should be considered as separate indicators. The study involved conducting an exploratory factor analysis (EFA) on 18 items with a group of 154 respondents. The EFA revealed a primary factor which encompassed 13 of the items. Additionally, two other factors emerged, represented by the remaining 5 items. To further validate the scale, a confirmatory factor analysis (CFA) was conducted with 147 participants. This group included both original participants (n=74), reassessed after six months, and new participants (n=73). The CFA results supported the initial findings, confirming the loadings of the 13-item WQPS on a single factor. The scale exhibited strong internal consistency, as evidenced by Cronbach's alpha coefficients of 0.91 and 0.89 in the two samples. Moreover, the WQPS showed convergent validity with the Household Water Insecurity Experiences Scale (HWISE), with correlation coefficients of -0.41 and -0.49 in the respective samples. Chapter 4 investigates the relationship between the combined effect of organoleptic perceptions of tap water and trust in the water utility on drinking water source preference amongst Puerto Rican consumers. The research question is; “How do organoleptic perceptions of drinking water and consumer trust in water utilities jointly affect the choice of drinking water sources in Puerto Rico?” To answer these, 154 surveys were collected from May 2022 to July 2022 in the municipalities of Loíza, Comerío, and Aguas Buenas. To investigate the associations between organoleptic perceptions of drinking water, trust in Puerto Rico Aqueduct and Sewer Authority (PRASA), and choices of drinking water sources, descriptive statistics and correlations were calculated initially. Logistic regression analyses were then utilized to assess how these variables incrementally predict the choice of drinking water sources. An alpha level of 0.05 was set to determine statistical significance. The results show that the predictive accuracy of preferences for tap versus bottled water sources improves when factors like color perception, odor perception, and trust in the water utility (PRASA) are considered alongside taste perception of tap water. This indicates that taste perception alone is not sufficient to fully understand people's drinking water preferences. Including these additional factors provides a more complete and accurate assessment of why people choose certain drinking water sources. Finally, chapter 5 of the dissertation focuses on exploring the relationship between different drinking water sources and psychological resilience among older adults in low-income areas of Puerto Rico. Psychological resilience is defined as the ability of individuals to recover from traumatic experiences, such as hurricanes. The research question posed is, "How do tap and alternative drinking water sources, like bottled water and well water, affect the psychological resilience of older adults in these Puerto Rican communities?" The study utilizes survey data collected from 209 respondents during the summer of 2021 in the municipality of Loíza. This data was analyzed using linear regression models to assess the impact of water source choices on psychological resilience. The results suggest that older adults who drink tap water show higher levels of psychological resilience compared to those who consume bottled or well water. A further gender-specific analysis revealed distinct patterns. Among men, there was only a slight difference in psychological resilience based on whether they consumed tap water or not, with a marginal decrease in psychological resilience observed among tap water consumers. In contrast, a significant difference in psychological resilience was found among women, with those drinking tap water exhibiting higher levels of psychological resilience compared to those who did not consume tap water. This dissertation makes a significant contribution to both academic literature and practical applications by offering a detailed analysis of water insecurity's role in post-disaster recovery in Puerto Rico. The research has several key implications that can guide water utility operators, policymakers, and other stakeholders in enhancing water safety and reliability. Firstly, the dissertation identifies consequences of not providing reliable access to clean piped water in homes. This finding is vital for water utility operators and policymakers, as it calls for immediate action to improve water infrastructure and access, particularly in the aftermath of disasters. Secondly, the dissertation brings to light the knowledge-behavior gap in utility management. This gap has significant implications for both utility companies and consumers. By understanding why there is a disconnect between the functioning of utilities and consumer water consumption patterns, utility companies can devise more effective strategies. These strategies could include a combination of education, incentives, and easily accessible information to empower consumers to make informed decisions and trust in the efficient use of water resources. Thirdly, the research provides insights into the empirical water quality compared to consumers' perceptions of water quality in Puerto Rico. This comparison is crucial for policymakers and local organizations, enabling them to identify and address discrepancies between actual water quality and perceived water quality. Addressing these discrepancies is essential in the ongoing recovery efforts and in improving water quality sustainably. Finally, the dissertation highlights the impact of water insecurity on the mental well-being of Puerto Rican consumers. Understanding the relationship between water insecurity and mental health aspects such as psychological resilience is pivotal. This understanding can guide water utility providers and policymakers in developing focused interventions and strategies aimed at enhancing the mental health of those affected by water insecurity. Overall, the dissertation offers a comprehensive framework for addressing water insecurity in Puerto Rico, with implications that extend beyond immediate recovery efforts to long-term policy and practice improvements in water quality management and mental health support

Assessing Spatial Consistency using Spatio-Temporal Interactions with Generalized Additive Models

Crop yield is spatially consistent when the high-yield areas of a field (or low-yield areas) occur in the same places from year to year. Using precision ag data from previous years to design a management plan for the current year assumes there is spatial consistency, but there are few statistical methods to assess this consistency, especially for data from more than two years. We use generalized additive models with tensor-product splines to smooth spatially-explicit annual yield data and project each year’s data onto the same grid of locations. Each year’s yield data is then centered and standardized to adjust for annual differences in overall yield and within field variability. The interaction between space and time tests the overall lack of spatial consistency. This can be implemented by comparing the fit of annual spatial-trend models to the fit of a model with a single spatial trend common to all years. Consistency at individual locations can be quantified by the standard deviation of annual adjusted yields at each location; this can be mapped to identify consistent and inconsistent areas of the field. We illustrate the approach using five years of data from a field in central Iowa

Augmented reality and consumers’ willingness to pay in mobile e-commerce. Does AR increase consumers’ willingness to pay in an online auction

E-commerce platforms face the challenge of enabling customers to assess the utility of products in their intended environments, particularly in the home furniture sector. Augmented Reality (AR) presents a promising solution, with major companies like IKEA, Wayfair, and Overstock introducing AR applications. Nevertheless, a significant knowledge gap exists, prompting this research to delve into this void and scrutinize the economic value of AR in shaping consumers' Willingness to Pay (WTP) in the context of online auctions. This dissertation comprises three interconnected papers that collectively scrutinize the multifaceted impacts of AR on consumers' WTP in the specific realm of online auctions. The first paper investigates the influence of Augmented Reality (AR) in mobile e-commerce on consumers' perceived risks and WTP in online auctions. Grounded in perceived risk theory, the study addresses Perceived Psychological Risk, Perceived Social Risk, and Perceived Performance Risk. An online experiment involving 61 participants compared AR mobile e-commerce with a 3D mobile e-commerce interface, and data analysis utilized SPSS and Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that AR Mobile E-commerce significantly reduces perceived social risk, positively affecting WPT. However, AR in Mobile E-commerce does not substantially mitigate perceived psychological risk, and this risk dimension does not significantly affect WPT. Similarly, AR in Mobile E-commerce positively influences the reduction of perceived performance risk, but this risk dimension does not significantly influence WPT. Mediation analysis suggests that perceived social risk plays a crucial role as a mediator between AR in Mobile E-commerce and consumers' WPT. The second paper explores the impact of AR on consumers' WTP in an online auction context within mobile e-commerce, drawing on the experiential hierarchy model (EHM). The study posits that AR positively influences consumers' WTP compared to 3D product displays, triggering affective (enjoyment) and cognitive (perceived ownership and perceived product quality) responses, which subsequently influence behavioral responses (willingness to pay more). Analysis of 61 valid responses through PLS-SEM and SPSS 29 reveals that AR significantly enhances consumers' perceived enjoyment and perceived product quality, positively impacting their willingness to pay. However, perceived ownership does not directly affect willingness to pay. Demographic factors such as age, gender, purchase frequency, and income do not have a direct influence. Mediation analysis suggests that perceived enjoyment, perceived product quality, and perceived ownership do not significantly mediate the relationship between AR and WTP. The third paper addresses the lack of standardized AR application guidelines for e-commerce. Using sentiment analysis of 1,049 user reviews of the IKEA Place App, this study reveals predominant dissatisfaction with the app, leading to the development of a comprehensive set of AR mobile e-commerce design guidelines. The research also compares AR mobile e-commerce with traditional 3D versions, finding a statistically significant difference in usability, with the AR version considered more usable. However, there was no significant correlation between usability scores and participants' willingness to pay on both platforms. This study sheds light on AR's potential and challenges in e-commerce, offering insights into enhancing user experience and economic outcomes. In conclusion, this dissertation contributes to the understanding of how AR impacts consumers' WTP in the context of online auctions within e-commerce, addressing perceived risks, experiential responses, and design guidelines. These findings offer valuable insights for e-commerce businesses seeking to harness AR's potential to enhance the shopping experience and drive revenue growth

Enhancing precision livestock farming through advanced time-series forecasting techniques and anomaly detection

Anomaly detection, or outlier detection, within animal production systems has emerged as a critical focus area in the industry. The recent advances in Precision Livestock Farming (PLF) technology, designed to monitor animal health, behavior, and farm environments, have led to a significant surge in available data. This wealth of information necessitates robust methods for early issue detection and prompt intervention, which can significantly enhance farm management practices and improve animal welfare. The goals of this thesis were to validate the effectiveness of various anomaly detection methods, ranging from basic statistical approaches to novel machine learning models, for real-time anomaly detection in time-series swine water usage data. An innovative algorithm was introduced to detect signs of heat stress in swine by analyzing water usage patterns and their correlation with different barn environments. Additionally, a commercially available feed-weighing technology was evaluated on its capacity to monitor feed usage and reduce out-of-feed events. With the integration of advanced sensors, internet of things (IoT) devices, and data analytics tools, it is now possible to continuously monitor various parameters of animal production systems. These PLF technologies enable the detection of deviations from normal patterns, which may indicate health problems, behavioral changes, or environmental stressors. Early identification of such anomalies allows for timely interventions, potentially mitigating adverse effects on the animals and improving overall productivity. Furthermore, implementing anomaly detection systems within the framework of PLF contributes to proactive farm management, reducing the reliance on reactive measures that often come at a higher cost and may compromise animal welfare. By using data analysis techniques and leveraging machine learning algorithms, farms, researchers, and stakeholders across the animal production sector can achieve a more precise and comprehensive understanding of their operations, leading to better decision-making and enhanced sustainability. The advancement of anomaly detection technologies within animal production systems represents a transformative approach to modern farming. These systems not only support improved animal health and welfare but also offer significant benefits in terms of operational efficiency and farm profitability

Developing a four-axis cyber-physical traverse system for highly dynamic experimental fluid mechanics studies

Traditional experimental methods, including stationary, non-dynamic test configurations and tedious switching of mechanical components, create barriers to discovery in fluid mechanics. We created a four-axis cyber-physical system (CPS) to break those barriers. Our CPS can perform experiments dynamically and test a massive range of physical systems with only the aid of a computer. In creating the CPS, we first defined the logic process of properly replicating a purely physical system. We created a single-axis CPS to test the validity of our process and gain crucial knowledge of the hardware and software needed to make the four-axis CPS. Once the CPS was sufficiently developed, we performed validation of the system’s active and passive modes. We began an additional energy harvesting experiment, and the system is equipped to perform many future studies

Perspectives in strategic leadership: Charting the past and forging new frontiers

This research endeavors to holistically chart the field of strategic leadership using a novel methodology, recognizing the fragmentation of the field, as well as conducting research on its new frontiers. Recognizing the paramount significance of strategic leadership in the organizational landscape. It commences by highlighting the fragmented nature of the literature on strategic leadership, acknowledging the lack of a holistic understanding in scholarly discourse. The first study proposes a pioneering mixed-method review, merging quantitative and qualitative approaches to remedy this. This comprehensive examination employs a coding framework grounded in a contemporary definition of strategic leadership, alongside bibliometric and thematic clustering methodologies. Through this, the review strives to identify integration opportunities, trace the evolution, and discern emerging trends in the field. Importantly, it advocates a comprehensive methodology, recognizing the inadequacies of solely quantitative or qualitative analyses. The second study underscores the vital role of ethical traits among CEOs, particularly honesty-humility, in light of recent corporate ethical lapses. It introduces PATH (Personality Assessment Tool for HEXACO) as a solution to challenges arising from CEO inaccessibility and self-reporting bias. PATH leverages videometric analysis and machine learning techniques, focusing on honesty-humility, to predict HEXACO scores using transcribed text. While demonstrating reliability and convergent and divergent validity, it acknowledges its performance gap compared to established tools, possibly attributed to differences in sample sizes. The study advocates expanding PATH's training dataset to enhance its predictive accuracy. This research contributes to organizational behavior, strategy, and strategic leadership literature. Extending beyond the Big Five framework and incorporating the HEXACO model offers valuable insights into CEO personality within an ethical context, including potential markers of dishonesty. PATH's emergence addresses methodological limitations, enabling the exploration of CEO personality on a broader scale and facilitating an understanding of its implications for firm-level outcomes. This dual focus on strategic leadership and CEO personality enriches the discourse, emphasizing the need for ongoing PATH development through additional training and validation data