32584 research outputs found
Sort by
Can you travel too much? The emotional numbness effect of travel frequency
Get PDFBased on hedonic adaptation theory, this research explores how travel frequency impacts emotional numbness. Across one pilot study and five studies using secondary data and experimental data, we find travel frequency has an inverted U-shaped effect on emotional intensity. That is tourists’ emotional intensity at first rises and then falls as travel frequency increases. It introduces tourist expertise as a measurable resource and demonstrates that it plays a mediating role in this effect. Furthermore, mystery can moderate these effects. Specifically, high mystery can alleviate emotional numbness caused by excessive travel frequency. These new findings offer valuable insights for tourism professionals aiming to create effective service strategies that improve tourist emotions
Soil Temperature, Environment, and Moisture Monitoring Network—a low-cost sensing network for Alabama
Get PDFIn order to bolster drought assessment and forecasting capabilities in Alabama, The University of Alabama in Huntsville has developed a network of rapidly deployable, low-cost soil moisture, temperature, and environment monitoring stations (STEMMNet). This network provides near-real-time transmission of in-situ sensed, high temporal resolution soil data, offering an extension beyond climatological analysis into operational use. Stations are manufactured using commercially available, inexpensive hardware and use low-cost sensors which demonstrated comparable accuracy when evaluated against an existing research-grade soil moisture network. Months of testing in a variety of environments allowed for several system optimizations, yielding a robust network with a high uptime. Collaborations with outside agencies including Alabama Forestry and select National Weather Service offices proved the versatility and need for this network. This study aims to outline the design process, data flow, lab, and comparative performance analysis, network design, and outcomes of STEMMNet. Overall, the network has performed well and demonstrates the ability to obtain high-quality soil data from a low-cost, minimal footprint, rapidly deployable station
Spartan Daily, October 1, 2025
Get PDFVolume 165, Issue 17https://scholarworks.sjsu.edu/spartan_daily_2025/1060/thumbnail.jp
Decadal change in deep-ocean dissolved oxygen in the North Atlantic Ocean and North Pacific Ocean
Get PDFDeclining dissolved oxygen concentrations are documented at upper and mid ocean depths, but less is known about the deep ocean. Long time-series measurements of dissolved oxygen analyzed with Winkler titration over several decades reveal regional differences at six stations in the abyssal North Atlantic Ocean and North Pacific Ocean. A significant decline in dissolved oxygen was evident at two stations in the northeast Pacific Ocean at 4000–4200 m depth (Stations PAPA and M). A similar decreasing but insignificant trend was recorded in the Arctic region of the North Atlantic Ocean (HAUSGARTEN). However, there was no significant decrease in dissolved oxygen at two temperate stations in the North Atlantic Ocean (PAP, BATS) and at one tropical station in the central North Pacific Ocean (ALOHA) all at similar depths \u3e4000 m over similar time periods. Continued long time-series observations will be needed to monitor global deep ocean processes and the impact of changing climate. We compare these rare long-term observations with model estimations from historical (1850–2014) and projected (2015–2100) forcing under a continued high greenhouse gas emission scenario
Spartan Daily, October 7, 2025
Get PDFVolume 165, Issue 19https://scholarworks.sjsu.edu/spartan_daily_2025/1062/thumbnail.jp
Sunrise at the Salton Sea: environmental justice, land use change, and hydrosocial dynamics of solar energy transitions in the Imperial Valley, California
Get PDFCalifornia\u27s Imperial Valley, with lithium-rich geothermal brines, extensive flat agricultural fields with abundant desert sunshine, and access to transmission lines, is a leading case to explore interconnected themes around just energy transitions. Despite being the poorest county in California and one of the smallest, Imperial already provides 15% of the state’s solar electricity, and the region as a whole represents on the order of 25% of the state’s electric power capacity. This paper brings to light frictions over solar energy development that have emerged over the history of solar power development in the southern Salton Sea region. It describes the history of solar development in the county and contextualizes in the broader hydrosocial territory and political economy, including how energy development patterns in the region are connected to regional and global energy markets. The analysis is based on analysis of media and news articles, public comments to official proceedings, hearings to environmental review or similar processes, and interviews, and involves a case study tracked closely since 2008. The findings show how social resistance to solar projects can result in better land use outcomes, but also points to different types of hydrosocial reconfigurations and environmental justice issues facing rural communities within and beyond the region. In the arid western United States, solar energy development is mediated by disputes over of Colorado River water, tribal sovereignty and cultural resources, raising questions about how new enterprises can finance ecological restoration of the degraded Salton Sea
Unraveling the Enigma of Classification of Synthetic and Genuine Information Using Machine Learning and Explainable AI
No full textThere is a preponderance of AI-generated text everywhere today. Literature shows that there has been considerable success in detecting such text. This paper uses explainable AI (XAI) techniques to get insights into the workings of the machine learning models used to classify synthetic text. In detecting such text, this work analyzes synthetic and genuine information from visualization and explainability perspectives. The text is converted into vector embeddings using Robustly Optimized BERT Pretraining Approach (RoBERTa). Variational Autoencoders (VAEs) are used for visualization and Support Vector Machine (SVM) is used for classification. Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Integrated Gradients are used to explain the classification. The experiments are done on two different types of datasets. Despite the machine learning model achieving outstanding accuracy similar to the previous work in the literature, it was determined that there is no clear explanation of why the representation learning or the classification works so outstandingly well. The explainability techniques used show that the model focuses on words that do not clearly indicate that the text is synthetically generated. Visualization in two dimensions shows that the vector embeddings of both classes of text overlap significantly and that there is no clear separation of the representations learned
Identity and the Russian-Ukrainian War: Evidence from the American Community Survey
No full textThe Russian invasion of Ukraine in 2022 led to significant changes in ethnic identity and language preferences among U.S. residents. We observe an 18.9% increase in U.S.-born individuals identifying as ethnic Ukrainian from 2021 to 2022 and a 6.8% decrease for U.S.-born ethnic Russians, which cannot be attributed to prior trends or sampling variation. The changes among Ukrainians were more pronounced among college graduates and those living in Democratic-leaning counties. We document an increase in the use of the Ukrainian language among Ukrainian immigrants, which cannot be explained by prior trends or immigration patterns. We test for labor market discrimination, which might be higher in areas where the political environment is more anti-Russian or pro-Ukrainian, but do not find evidence for employment or wage discrimination, suggesting identity changes are driven by non-market factors
Navigating accountability: the role of paradata in AI documentation and governance
Get PDFPurpose: The increased use of Artificial Intelligence (AI) has prompted governments internationally to provide guidance and legislation to maximize the benefits of AI while minimizing the risks to humans and organizations. This paper explores how published requirements for documentation in a sampling of authoritative texts address the challenges of creating, capturing and preserving records of the design, implementation and use of AI tools for accountability and transparency, and how the analytical concept of paradata can help to meet the recordkeeping challenges presented by the design, development and implementation of AI systems. Design/methodology/approach: Inductive reading and conceptual analysis of a set of AI laws, regulations and frameworks published by the EU, UK, USA, Canada and Singapore. Findings: The authoritative texts reviewed clearly describe activities which imply the necessity of records creation and preservation. Identifying specific documents necessary to comprise a sufficient body of records to provide evidence of accountable AI implementation and operation can be difficult. Literature on paradata in archival applications of AI may prove productive in identifying relevant information artifacts for preservation in the AI process. Paradata is produced by those designing and implementing AI systems and by AI systems themselves. Practical implications: Identifying relevant paradata produced by AI systems requires archivists to develop both the capacity to analyze and the vocabulary to discuss these systems in order to preserve evidence of their operation in compliance with legislation and international standards. Originality/value: No comparable comparative analyses have been published in the archives and information field