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Data management plan for the grant, "Non-Genetic Inheritance of Hypoxia Tolerance in Fishes: Dynamics and Mechanisms." Research quantifying the inheritance of tolerance to low oxygen in a model fish and then determine the tolerance mechanisms, at organismal to molecular levels, that are passed on from parents to their offspring. The investigators will not only focus on conventional, well-studied genetic mechanisms for inheritance, but will explore so-called “epigenetic” forms of inheritance that may transfer parental characteristics for only a generation or two. Such “temporary inheritance” might actually require less energy and be more beneficial to a species than the more permanent form of genetic inheritance. This project will quantify non-genetic inheritance of hypoxia tolerance in zebrafish as a model organism and then identify underlying mechanisms, at organismal to molecular levels, in parents and in their progeny. Specifically, this project will quantify non-genetically inherited traits that allow hypoxia tolerance, determine “wash-in” and “wash-out” (i.e., the dynamics) of hypoxia-tolerant phenotypes across multiple generations, and establish epigenetic mechanism(s) of non-genetic inheritance in subsequent generations. The information provided by this project will allow biologists to better predict, and perhaps even mitigate, the negative consequences of future episodes of low oxygen in rivers and lakes.

This paper studies the long-term energy management of a microgrid coordinating hybrid hydrogen-battery energy storage. We develop an approximate semi-empirical hydrogen storage model to accurately capture the power-dependent efficiency of hydrogen storage. We introduce a prediction-free two-stage coordinated optimization framework, which generates the annual state-of-charge (SoC) reference for hydrogen storage offline. During online operation, it updates the SoC reference online using kernel regression and makes operation decisions based on the proposed adaptive virtual-queue-based online convex optimization (OCO) algorithm. We innovatively incorporate penalty terms for long-term pattern tracking and expert-tracking for step size updates. We provide theoretical proof to show that the proposed OCO algorithm achieves a sublinear bound of dynamic regret without using prediction information. Numerical studies based on the Elia and North China datasets show that the proposed framework significantly outperforms the existing online optimization approaches by reducing the operational costs and loss of load by around 30% and 80%, respectively. These benefits can be further enhanced with optimized settings for the penalty coefficient and step size of OCO, as well as more historical references. Submitted to Applied Energy

In the quest for effective solutions to address Environ. Pollut. and meet the escalating energy demands, heterojunction photocatalysts have emerged as a captivating and versatile technology. These photocatalysts have garnered significant interest due to their wide-ranging applications, including wastewater treatment, air purification, CO2 capture, and hydrogen generation via water splitting. This technique harnesses the power of semiconductors, which are activated under light illumination, providing the necessary energy for catalytic reactions. With visible light constituting a substantial portion (46%) of the solar spectrum, the development of visible-light-driven semiconductors has become imperative. Heterojunction photocatalysts offer a promising strategy to overcome the limitations associated with activating semiconductors under visible light. In this comprehensive review, we present the recent advancements in the field of photocatalytic degradation of contaminants across diverse media, as well as the remarkable progress made in renewable energy production. Moreover, we delve into the crucial role played by various operating parameters in influencing the photocatalytic performance of heterojunction systems. Finally, we address emerging challenges and propose novel perspectives to provide valuable insights for future advancements in this dynamic research domain. By unraveling the potential of heterojunction photocatalysts, this review contributes to the broader understanding of their applications and paves the way for exciting avenues of exploration and innovation.

The key to this study is to explore the relation between renewable energy and trade openness under climate change impacts on international trade. This study is based on the analyses of trade openness index and the Notre Dame-Global Adaptation Index in measures of research of the transfer of renewable energy technologies globally, promotion of the growth of renewable energy share, and the country's current vulnerability to climate disruptions. Research is focused on evaluation of low-carbon trade, analysis of renewable heat, and estimation of the impact of renewable energy on trade openness under climate disruptions. Research verified that development of renewable energy can reduce the human impact on climate change, but such technologies are not available to all countries; because of this, different countries may be affected by climate change to varying degrees. The implementation of renewable energy technologies should help countries become more resilient to climate change through the availability of appropriate means of producing renewable energy; however, this is possible subject to the country's openness to the arrival of goods and technologies from outside-increasing trade openness level. Countries with open and competitive markets are more inclusive to adopt and support the renewable energy technologies and their development. From another position, development of trade and deep integration of economy can create external dependence on imported energy sources and block the development of domestic renewable energy sectors. The economic challenges in the field of renewable energy need the focus of governments and policy makers on supporting the stability of renewable generation facilities, increasing the reliability of energy supply, protection from seasonal changes in demand for electricity, support of the energy cooperatives and private consumers of renewable energy, and transition the public transport sector for renewable energy sources. Further research in this direction has prospects, because globalization is a stable trend of recent decades, and allows for an even distribution of efforts to produce various products and provide them to the population, but decarbonization potential of renewable energy production is untapped as fossil fuels dominate in largest world markets.

Lake ecosystems are extremely sensitive to nitrogen growth, which leads to water quality degradation and ecosystem health decline. Nitrogen depositions, as one of the main sources of nitrogen in water, are expected to change under future climate change scenarios. However, it remains not clear how nitrogen deposition to lakes respond to future meteorological conditions. In this study, a source-oriented version of Community Multiscale Air Quality (CMAQ) Model was used to estimate nitrogen deposition to 263 lakes in 2013 and under three RCP scenarios (4.5, 6.0 and 8.5) in 2046. Annual total deposition of 58.2 Gg nitrogen was predicted for all lakes, with 23.3 Gg N by wet deposition and 34.9 Gg N by dry deposition. Nitrate and ammonium in aerosol phase are the major forms of wet deposition, while NH3 and HNO3 in gas phase are the major forms of dry deposition. Agriculture emissions contribute to 57% of wet deposition and 44% of dry deposition. Under future meteorological conditions, wet deposition is predicted to increase by 5.5% to 16.4%, while dry deposition would decrease by 0.3% to 13.0%. Changes in wind speed, temperature, relative humidity (RH), and precipitation rates are correlated with dry and wet deposition changes. The predicted changes in deposition to lakes driven by meteorological changes can lead to significant changes in aquatic chemistry and ecosystem functions. Apart from future emission scenarios, different climate scenarios should be considered in future ecosystem health evaluation in response to nitrogen deposition.

[This dataset contains all data used for Studies 2 (qualitative), 3 (quantitative survey) and 4 (longitudinal) in my PhD research.]<br>Thesis abstract:This thesis explores the potential positive impact of artificial intelligence (AI) technology on sustainability in and outside of the tourism industry through four studies. Study 1 introduced the AI4GoodTourism framework, emphasising the need for sustainability inclusion</em> and tourist involvement</em> to achieve a successful sustainability transition. Five themes were identified through a systematic review: intelligent automation to enhance tourist experience, preserve heritage, promote quality of life, measure tourist experience, and preserve the environment. The latter theme was the least explored scholarly topic. Study 2 conceptualised a conversational AI chatbot to promote pro-environmental behaviour spillover among tourists visiting the Gili Islands, Indonesia. A theoretical model was proposed, highlighting factors influencing chatbot usage and spillover effects. Study 3 identified relationships between factors from Study 2, revealing that factors such as performance expectancy, timing, </em>and credibility</em> significantly influenced people’s intention to use the proposed chatbot technology. A significant relationship was established between people’s intentions to use the chatbot and environmentally friendly transport. Scenario-based experiments showed that using the chatbot with educational information on sustainability was sufficient to trigger behaviour change. Study 4 explored the underlying mechanism of pro-environmental behaviour spillover through human-chatbot interactions using flashback nudging. A longitudinal experiment involving the Gili tourists demonstrated that flashback nudging delivered through chatbot technology strengthened their environmental self-identity, leading to significant differences in self-reported pro-environmental behaviour between treatment and control groups. In conclusion, the thesis demonstrates that AI technology, designed with high sustainability inclusion, can positively impact sustainability through tourists’ marginal contributions. The proposed AI4GoodTourism framework and the conceptualised chatbot technology, especially with flashback nudging, show potential for facilitating pro-environmental behaviour spillovers among tourists. All four studies in this thesis highlight the importance of prioritising sustainability in AI innovations for the tourism industry, offering insights for future AI development and adoption to support the global sustainability agenda.

# Data from: Evaluating the effects of cotton intercropping on cool-season perennial forage persistence, forage mass, and nutritive value in the southeastern United States This dataset was used to generate 3 figures and 5 tables in the publication, "Evaluating the effects of cotton intercropping on cool-season perennial forage persistence, forage mass, and nutritive value in the southeastern United States". All data was collected in Florence, South Carolina at the Clemson University Pee Dee Research and Education Center during 2021 and 2022. \#Description of dataset and file structure Data is presented in an Microsoft Excel Spreadsheet, with separate tabs for datasets related to each of the 3 figures/subfigures and 5 tables in the published manuscript. For all data the following treatment abbreviations are used: Fallow = weedy, unplanted treatment ARG = annual ryegrass RC+WC = 50/50 mixture of red and white clover ARG+RC+WC = 50% annual ryegrass, 25% red clover, and 25% white clover **Data for Figure 1** These data were used to generate Figure 1, featuring mean weather data for the study years, 2021 & 2022, along with 30-year mean weather data for the nearest NOAA weather station (Florence, SC Regional Airport). Units are provided in the column headers. **Data for Figure 2a & 2b** These data were used to generate Figures 2a and 2b, featuring the amount of spring forage mass accumulation preceding and in between cotton intercropping. Forage mass in the RC+WC and ARG+RC+WC treatments consited of a mix of weeds and clovers, while ARG and fallow treatments are entirely comprised of weedy biomass **Data for Figure 3a & 3b** These data were used to generate Figures 3a and 3b, featuring the red and white clover populations in each treatment for each year of the study. **Data for Figure 3c** These data were used to generate figure 3c, featuring the weedy species population changes from spring to fall before, between, and after two seasons of cotton intercropping in 2021 and 2022.Final data in the published figure was Weeds per square meter. **Data for Tables 1 and 2** These data were used to generate Tables 1 & 2, featuring height data for individual clover, annual ryegrass, and weedy species observed among different treatments. **Data for Table 3** These data were used to generate part of table 3, featuring the forage nutritive value data (crude protien, CP; acid detergent fiber, ADF; neutral detergent fiber, NDF; non-fibrous carbohydrates, NFC; total digestible nutrients, TDN; net energy of lactation, NEL; net energy of maintenance, NEM; net energy of gain, NEG) **Data for Tables 3, 4, and 5** These data were used to generate part of Table 3, and Tables 4 and 5, featuring nutrient compositions of the forage plant tissues collected during the trial. All data are in g/kg dry matter. ## Sharing Access Information These data were originated from the published manuscript: [https://doi.org/10.1002/agj2.21625](https://doi.org/10.1002/agj2.21625). This is digital research data corresponding to a published manuscript, Evaluating the effects of cotton intercropping on cool-season perennial forage persistence, forage mass, and nutritive value in the southeastern United States, in Agronomy Journal. Integrated forage–row cropping systems provide important agronomic and economic benefits to producers. However, little attention has been given to incorporating forages into row crop systems unique to the southeastern United States. This study assessed the viability of intercropping cotton (Gossypium hirsutum L.) on perennial, cool-season legumes during the summer months in the Southeast Coastal Plain over two production years. Treatments included a weedy fallow, annual ryegrass (ARG; Lolium multiflorum Lam.) monoculture, a red clover (RC; Trifolium pratense L.) and white clover (WC; Trifolium repens L.) mixture, and a three-species mixture of ARG, RC, and WC. Plots were established in fall 2020 with forage grown until May 2021 and 2022, when plots were strip-tilled and planted with cotton. Cotton was managed with minimal herbicide use to preserve perennial clovers. Data was collected over two years (October 2020 - October 2022) at the Clemson Pee Dee Research and Education Center near Florence, SC. Data was collected by field measurements of plant height, biomass accumulation, and species persistence and diversity, with laboratory assays conducted to collect plant nutritional composition. Forage nutrtitive value parameters and fiber content were conducted by a third-party laboratory (Dairy One LLC, Ithaca, NY).

Energy and buildings 326, 114987 (2025). doi:10.1016/j.enbuild.2024.114987 Published by Elsevier Science, Amsterdam [u.a.]