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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.

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.

1H and 13C nuclear magnetic resonance spectra were recorded on a Bruker Avance III 400 or Magritek Spinsolve 60 spectrometer at 293 K. Chemical shifts are reported as δ in parts per million (ppm) and referenced to the chemical shift of the residual solvent resonances (CDCl3: 1H: δ = 7.26 ppm, 13C: δ = 77.16 ppm). Polymer molecular weight and dispersity were determined using a Malvern Viscotek GPCmax size exclusion chromatograph instrument fitted with a Viscotek TDA 305 detector unit equipped with refractive index and light scattering detectors. Samples were dissolved in tetrahydrofuran at a concentration of approximately 1 mg mL-1 and eluted through a guard column and two Agilent PLGel 5 µm mixed C columns (300 x 7.5 mm) at a flow rate of 1 ml.min-1; the elution pathlength was heated to 30 °C for the duration. Molecular weights were calibrated against known poly(methyl acrylate) standards. Differential scanning calorimetry was conducted using a TA Instruments Discovery 2500. Samples were analysed in non-hermetic aluminium pans and compared against an empty reference pan of the same type. Loaded sample masses were between 3 and 10 mg. Samples were subjected to two complete heat/cool cycles from -50 °C to 150 °C (-85 °C to 150 °C for lower Tg samples) and both heating and cooling rates were set at 10 °C min-1. UV/Vis transmittance and absorption spectra were measured with a PerkinElmer Lambda 750 spectrophotometer. Transmittance spectra of films were measured using wavelength scan with a resolution of 1 nm at a scan speed of 267 nm/min and a slit width of 2 nm. Samples were directly mounted to the sample holder. Solution spectroscopy was carried out on solutions in THF in quartz SUPRASIL® cuvettes (10 mm pathlength). Absorption spectra of luminophore solutions were taken using a wavelength scan with a resolution of 0.5 nm at a scan speed of 141.20 nm/min and a slit width of 2 nm. A reference sample of THF in an identical cuvette was used to apply a 100% transmission correction. Steady-state PL spectroscopy was performed on a Fluorolog-3 spectrophotometer (Horiba Jobin Yvon). Solid-state emission spectra were recorded using the front-face configuration. Solution emission spectra were recorded using the right-angle configuration, over 10 averaged scans. The excitation and emission slits were adjusted so that the maximum PL intensity was within the range of linear response of the detector and were kept the same between samples if direct comparison between the emission intensity was required. Emission and excitation spectra were corrected for the wavelength response of the system and the intensity of the lamp profile over the excitation range, respectively, using correction factors supplied by the manufacturer. Photoluminescence quantum yields (ΦPL) were measured using a Quanta-phi integrating sphere (Horiba Jobin Yvon) mounted on the Fluorolog-3 spectrophotometer. The UC emission and phosphorescence spectra, threshold intensity (I_th), UC quantum yield (UC) and lifetime measurements were performed using an FLS1000 time-correlated single photon counting (TCSPC) spectrometer (Edinburgh Instruments Ltd.). The samples were excited with a 532 nm laser (MGL-III-532, 200mW). To determine I_th, the laser power was adjusted using a Thorlabs PM100A Power Meter Console combined with a S120VC Si photodiode power sensor (range: 200-1100 nm) before the measurement, across the 5 to 8000 mW cm-2. The ΦUC was measured with an integrating sphere (SNS125 5-inch sphere, three windows, International Light Technologies). The sample was placed at the center of the sphere using a sample holder. A baffle is placed in front of the observation window, which blocks any scattering and reflection of the laser from the sample surface. The angle of the sample holder is adjustable. The normal direction of the sample holder is 22.5˚ to the excitation beam line, which leads the reflection of the laser to the inner surface of the sphere. The laser power was measured with a photodiode before each ΦUC measurement. Both the emission of the sample (380-500 nm) and scattering of the laser beam (530-534 nm) were measured. A neutral density filter (O.D.=3.0) was placed before the excitation beam for the scattering intensity measurements. Six data sets were collected to calculate the ΦUC of each sample: 1. sample in the path of the beam – “in fluorescence”; 2. sample in scattering; 3. sample facing away from beam – “out of fluorescence”, 4. sample out of scattering; 5. empty sphere fluorescence; 6. empty sphere scattering. Fluorescence decay measurements were performed using the multi-channel scaling (MCS) method on a the FLS1000 TCSPC spectrometer. The emission decay was recorded using a photomultiplier tube (PMT-980) equipped with TCC2 counting electronics. For the upconversion lifetime measurements, a wavelength of 440 nm was selected, and a short-pass filter (cut-off at 500 nm, Thorlabs) was placed in front of the detector. For the phosphorescence lifetimes, a wavelength of 660 nm was selected, and a long-pass filter (cut-off 550 nm, Thorlabs) was used. The instrument response function (IRF) was measured using Ludox® colloidal silica solution (a SiO2 particle suspension solution) and using a neutral density filter (O.D.=3) to attenuate the laser intensity. The pulse repetition rate was adjusted to ensure the full decay was detected within the time window. Data-fitting was carried out by tail fitting to each emission decay trace using a multiexponential decay function within the FAST software package (Edinburgh Instruments Ltd.). The goodness of fit was evaluated using the reduced chi-square statistics (χ2) and the randomness of the residuals. Please also see the readme file for more details on data collection and file organisation.

# 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).