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Mini Worldlit: A Dataset of Contemporary Fiction from 13 Countries, Nine Languages, and Five Continents
Full text linkWorld literature plays a key role in understanding the global diversity of human storytelling. However, datasets suitable for large-scale cross-cultural analysis remain limited. Responding to the increasing digitization of literary texts and the need for more diverse and multilingual resources, we introduce Mini Worldlit, a manually curated dataset of 1,192 works of contemporary fiction from 13 countries, representing nine languages across five continents. Mini Worldlit employs consistent cross-cultural selection criteria, overseen by scholarly experts, to ensure geographic, linguistic, and stylistic coherence. The dataset provides a foundation for future comparative studies of global literary cultures, offering a template for cross-cultural sampling. Our methodology pairs geographic boundaries with linguistic communities, enabling a structured exploration of world literature. This dataset is designed to facilitate a comparative approach to understanding literature and support the growing field of multilingual digital humanities
Human capital generative potential: cultivating intrapreneurial employees in sport and recreation organizations
No full textRationale/purpose: The purpose of this study is to examine the relationships among recreational sport organization employees’ engagement, resilience, and the ability to design creative mechanisms to overcome work challenges. Employees in this study all faced similar challenges and uncertainty, which provided an opportunity to explore psychological well-being, innovation, and levels of engagement with intrapreneurship. Design/methodology/approach: In-depth, semi-structured interviews were conducted with 16 campus sport and recreation center employees across 13 organizations from 10 states in the United States. Findings: Findings and discussion focus on the importance of interpersonal interactions, the ways in which personal challenges enabled adaptation and intrapreneurship, and the generative potential of recreation and sport employees in relation to psychological well-being, innovation, and intrapreneurship. Practical implications: In adapting to new, challenging work situations, employees can have a heightened sense of self-determination and growth. The self-renewal sub-component of intrapreneurship is closely associated with reimagined organizational strategy when facing change and organizational obstacles. Research contribution: Employees serve an important role in improved internal operations in sport and recreation organizations via innovation and intrapreneurship
Complex Models of Sequence Evolution Improve Fit, But Not Gene Tree Discordance, for Tetrapod Mitogenomes
Full text linkVariation in gene tree estimates is widely observed in empirical phylogenomic data and is often assumed to be the result of biological processes. However, a recent study using tetrapod mitochondrial genomes to control for biological sources of variation due to their haploid, uniparentally inherited, and non-recombining nature found that levels of discordance among mitochondrial gene trees were comparable to those found in studies that assume only biological sources of variation. Additionally, they found that several of the models of sequence evolution chosen to infer gene trees were doing an inadequate job of fitting the sequence data. These results indicated that significant amounts of gene tree discordance in empirical data may be due to poor fit of sequence evolution models and that more complex and biologically realistic models may be needed. To test how the fit of sequence evolution models relates to gene tree discordance, we analyzed the same mitochondrial data sets as the previous study using 2 additional, more complex models of sequence evolution that each include a different biologically realistic aspect of the evolutionary process: A covarion model to incorporate site-specific rate variation across lineages (heterotachy), and a partitioned model to incorporate variable evolutionary patterns by codon position. Our results show that both additional models fit the data better than the models used in the previous study, with the covarion being consistently and strongly preferred as tree size increases. However, even these more preferred models still inferred highly discordant mitochondrial gene trees, thus deepening the mystery around what we label the “Mito-Phylo Paradox” and leading us to ask whether the observed variation could, in fact, be biological in nature after all
IIScan: Detection and Analysis of IIS Native Modules in Volatile Memory
Full text linkThe Internet Information Services (IIS) is a Microsoft-developed web server designed with a modular architecture to foster extensibility. Its worker process loads all IIS modules when the server receives a request, and native modules operate with the same level of access as the worker process. The built-in persistence and resource access make malicious modules powerful tools post-compromise. This thesis focuses on identifying all native modules in the system by analyzing volatile memory. Through binary analysis of the worker process, we identify critical data structures containing information about system modules. We developed two Volatility plugins to assist in detecting these modules and extracting critical information, offering valuable tools for memory forensics of IIS web servers
Identifying Saharan Air Layer Events and their Relationship to Instability and Rainfall Patterns in the Tropical North Atlantic Ocean
No full textEvery year, strong North African winds across the Sahara loft dust particles and advect them westward within the Saharan Air Layer (SAL). These plumes of dust often reach the Caribbean Sea and can heavily affect regional weather patterns by suppressing convection. When concentrations are high, decreased rainfall can yield negative societal and ecological impacts, potentially leading to drought. The Puerto Rican early rainfall season (ERS), spanning from 1 April through 31 July, overlaps with Saharan dust migration periods, and systematically detecting instances of SAL activity near the island is important for understanding longer-term drought-forcing trends in the region.
Corridors of high-percentile column-integrated dust flux were extracted from the MERRA-2 reanalysis data set to delineate between unique SAL events using methods similar to that of atmospheric river detection. The resulting dataset, lasting from 1980–2023 for the ERS months, reveals high variability in both SAL plume frequency and intensity throughout the tropical Atlantic. The frequency of ERS SAL dust plumes has increased over the 44-year study period by 0.5 days per year. The regions of maximum frequency vary annually and mostly center in the eastern tropical Atlantic. Using convective instability parameters for tropical convection, the influence of the SAL on instability is stronger in the Caribbean than farther west. Rainfall relationships during the ERS are less conclusive, as events often occur independently of the SAL, though strong SAL activity slightly reduces rainfall.
This study develops an objective SAL event database to assess the vulnerability of the Caribbean islands to dust, determine their role as a hydrometeorological forcing mechanism, project future SAL frequency, track prototypical Atlantic migration, and analyze its impact on mesoscale and synoptic convective instability
MULTI-SCALE MODELING OF ELECTROCHEMICAL SEPARATION: FROM MATERIAL MODELING TO PROCESS OPTIMIZATION
No full textElectrochemical systems are essential for sustainable water purification, energy generation, and material separations. However, their design and optimization are often hindered by nonlinear dynamics, limited datasets, and incomplete data. This dissertation addresses these challenges by developing hybrid modeling frameworks that integrate compositional modeling, molecular dynamics (MD), machine learning (ML), reinforcement learning (RL), transfer learning (TL), and machine learning potentials (MLP). These approaches bridge the gap between mechanistic and data-driven models, enabling accurate predictions, optimization, and scalable solutions for electrochemical systems.
The research begins with the development of an MD-ML framework to predict ion activity coefficients in ion-exchange membranes (IEMs), reducing reliance on extensive experimental data and eliminating the need for new fitting parameters typical of existing activity models. Next, a hybrid modeling approach for brackish water desalination via electrodialysis (ED) and electrodeionization (EDI) is introduced. This approach combines information from mathematical models with ML-based surrogate models to achieve high predictive accuracy and multi-objective optimization, enabling the design of conditions with high separation efficiency and low energy consumption. Additionally, an RL-based control framework is proposed, allowing for the autonomous optimization of operational parameters to improve ion removal efficiency.
Furthermore, a TL-based strategy is developed to address incomplete datasets in capacitive devices, combining data imputation with ML to enable accurate modeling and exploration of experimental conditions using multi-objective optimization. Finally, a physics-informed MLP framework is introduced to simulate ion transport in membrane-ion-water systems, using neural network potentials trained on ab initio molecular dynamics (AIMD) data for scalable and transferable modeling of structural and dynamic properties.
This dissertation presents novel methodologies that enhance data-driven modeling efforts in electrochemical separation processes. These methodologies demonstrate the effective incorporation of domain knowledge into data-driven models, facilitating a bidirectional flow of information between mechanistic and empirical models. This work advances the design, control, and optimization of electrochemical systems, paving the way for more efficient, scalable, and sustainable technologies
Brooding, Reflection, and Anger Rumination Relate to Suicidal Ideation through the Role of Thought Control
No full textDespite the far-reaching impact of suicide on our communities, suicide prevention has historically focused on distally related risk factors for suicidality, which gives us an incomplete picture of how someone comes to make a suicide attempt. Instead, our focus needs to extend to research that explains the maintenance and progression from an emotional state to a suicidal crisis. One such factor, rumination, may create or worsen suicidal thinking by amplifying the distress associated with negative thoughts. Ruminative thoughts are often described as difficult to control, and people may think about suicide as an escape from these uncontrollable thoughts. The current study examined the relationship between severity of lifetime suicidal thinking and certain forms of rumination (i.e., brooding, reflection, anger rumination, and suicidal rumination) in a sample of 145 undergraduate students with suicidal thoughts. For each form of rumination that was related to suicidal thinking, we then examined whether that relationship was accounted for by perceived uncontrollability of one’s own thoughts. We found that all forms of rumination were related to severity of lifetime suicidal thinking, as well as heightened perceived inability to control one’s own thoughts. This thought control inability helped account for the relationships between brooding, reflection, and anger rumination with severity of suicidal thinking, but did not play a role in the relationship between suicidal rumination and suicidal ideation severity. Clinicians should be aware of the impact ruminative thoughts may have on suicidal thinking. More research needs to be done to replicate and extend these effects
Beyond Reoffending and Rearrest: Expanding the Collateral Consequences of Formal Processing to Youth Homelessness
Full text linkThere is robust evidence that juvenile justice contact during formative years is associated with deleterious outcomes. The effect of juvenile court intervention on youth homelessness, however, has received scant empirical attention despite evidence that many justice-involved youth are represented in the unhoused youth and adult population. This study leverages data collected from the Crossroads Study (N = 1115), a longitudinal, multi-site study to explore the impact of formal processing on subsequent experiences with homelessness. This study found that even when accounting for the effect of detention and other factors associated with youth homelessness, youth who were formally processed were twice as likely to report living on the streets compared to their informally-processed peers. These findings highlight the need for researchers to move beyond investigations of criminal justice outcomes to reveal the other ways in which justice-system involvement during adolescence can alter life course trajectories and expose youth to adverse experiences
Empirical Analysis of Incentivized Energy Assessment Recommendations and IIoT Cost-effectiveness in U.S. SMEs
No full textThe industrial sector in the United States accounted for 33% of total energy consumption in 2022. Small and medium-sized enterprises (SMEs), which comprise 99.9% of U.S. businesses present a critical opportunity for energy efficiency improvements. Although energy audits provide actionable Assessment Recommendations (ARs) to reduce consumption, high implementation costs remain a key barrier to adoption.
While prior studies have explored rebate programs, most are limited to residential and commercial sectors or focus narrowly on specific technologies or geographic areas for the industrial sector. The objective of this study is to empirically investigate the “Incentivized Assessment Recommendation” (IAR) trends and to evaluate the cost-effectiveness of IIoT adoption as a representative “Not Incentivized Assessment Recommendation” (NIAR). This research addressed two research questions: (1) How frequently are IARs issued, and which types are most common across U.S. SMEs? (2) Do the estimated annual cost savings from IIoT, as a case study for NIARs, significantly exceed their initial implementation costs?
To address these questions, the study analyzed 164,308 ARs from 22,097 industrial assessments in the publicly available Industrial Training & Assessment Center (ITAC) database. Descriptive statistics were used to assess IAR trends, geographic distribution, and implementation rates. A non-parametric Mann-Whitney U test was applied to evaluate the cost-effectiveness of IIoT adoption by comparing implementation costs (IMPCOST) and projected annual savings (PSAVED).
Results indicated that IARs gradually increased since their emergence in 1991, reaching 20% of all ARs by 2024. However, IARs remain a minority in comparison with NIAR. The most common IARs were related to lighting and motor systems. Nonetheless, implementation rates varied, with some IARs, particularly solar energy systems, showing low adoption despite rebate eligibility. Additionally, the statistical analysis of IIoT ARs confirmed a significant difference between implementation cost and projected annual savings of IIoT-related recommendations.
Finally, the outcome of this research can provide insights into the adoption patterns of IARs and cost effectiveness of IIoT as a case study of NIARs to support more informed decision-making for SMEs and guide future energy policy development
AI-Based Object Detection and Risk Identification for Enhanced Construction Site Safety
Full text linkRecent advancements in computer vision for construction site safety have encountered significant hurdles, especially in the nuanced tasks of object detection and the identification of unsafe worker behaviors. These challenges are often exacerbated by complex and cluttered backgrounds, wide variations in object scale, and inconsistent image quality. While existing methodologies have utilized attention mechanisms to analyze spatial and temporal features, they frequently neglect the benefits of adaptive sampling and channel-wise feature adjustments, thereby failing to exploit potential spatiotemporal redundancies. This thesis introduces a two-pronged approach to address these limitations. First, we propose the Optimized-Position Network (OP-Net), a novel architecture for object detection. The core of OP-Net is the Optimized Position (OP) module, which significantly enhances the relationships between feature channels by leveraging global feature affinity associations. This allows for a more robust and accurate detection of various objects on a construction site. Second, we present an innovative attention-based spatiotemporal sampling strategy designed to efficiently and accurately identify unsafe actions. This adaptive sampling method dynamically allocates computational resources, focusing on the most salient spatial regions and temporal moments in video data. This targeted approach minimizes redundant processing while maximizing the capture of critical information related to unsafe behaviors. To validate our proposed methods, we conducted extensive evaluations on two challenging, large-scale datasets. The object detection capabilities of OP-Net were rigorously benchmarked on the SODA (Site Object Detection) dataset, where it demonstrated superior performance in terms of both accuracy and efficiency. Furthermore, our unsafe action identification model was evaluated on the CMA (Construction Motion Analysis) dataset. The results show that our model not only achieves new state-of-the-art performance in accuracy but also maintains a reasonable level of computational efficiency, making it a practical solution for real-world deployment in construction safety monitoring systems